System and method for predicting the risk of future lung cancer

ABSTRACT

Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

CROSS REFERENCE

This application claims the benefit of and priority to U.S. Provisionalpatent Application No. 62/962,614 filed on Jan. 17, 2020, the entiredisclosure of each of which is hereby incorporated by reference in itsentirety for all purposes.

BACKGROUND

Lung cancer most commonly begins with the development of a lung nodule.A nodule may be cancerous or may be a benign overgrowth of tissue thatsubsequently undergoes malignant transformation. The radiologiccommunity recognizes this process and, in an attempt to standardizemedical management, developed a scoring system to stage nodulesdiscovered on CT scan to assess their likelihood of being cancer. Thisstaging system is called Lung CT screening, Reporting and Data System(Lung-RADS) and is based upon the size of the nodule, the rate of growthof the nodule and the appearance of the nodule. Generally, the largerthe nodule, the more rapid its growth or the more irregular it is inappearance, the more likely it is to be cancer.

However, in many scenarios, lung nodules in patients remain undetectedfor periods of time or, even if detected, can already indicate anadvanced stage of cancer. Therefore, there is a need for earlyprediction of lung cancer risk in patients even prior to the developmentof one or more lung nodules.

SUMMARY

Embodiments of the invention disclosed herein involve implementing riskpredictions models to analyze images (e.g., CT scans) for predictingfuture risk of lung cancer. Risk prediction models analyze featuresextracted from images, such as nodule specific features and non-nodulespecific features. In various embodiments, inclusion of non-nodulespecific features in the risk prediction model has the benefit ofenabling the risk prediction models to generate future risk of cancerpredictions for subjects that do not yet have a lung nodule. Forexample, by analyzing non-nodule specific features, examples of whichinclude lung parenchyma features and/or body composition features, riskprediction models can be implemented for the early detection of lungcancer prior to the development of a lung nodule. In variousembodiments, risk prediction models can be implemented to predictwhether a subject is likely to develop cancer in the next year. Invarious embodiments, risk prediction models can be implemented topredict whether a subject is likely to develop cancer in the next 3years. In various embodiments, risk prediction models can be implementedto predict whether a subject is likely to develop cancer in the next 5years. In various embodiments, risk prediction models can be implementedto predict whether a subject is likely to develop cancer within a M timeperiod. In various embodiments, M is any of 6 months, 1 year, 1.5 years,2 years, 2.5 years, 3 years, 3.5 years, 4 years, 4.5 years, 5 years, 5.5years, 6 years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5 years, 9years, 9.5 years, 10 years, 10.5 years, 11 years, 11.5 years, 12 years,12.5 years, 13 years, 13.5 years, 14 years, 14.5 years, 15 years, 15.5years, 16 years, 16.5 years, 17 years, 17.5 years, 18 years, 18.5 years,19 years, 19.5 years, or 20 years.

Risk prediction models are useful for developing preventive therapiesfor lung cancer by enabling clinical trial enrichment and ultimatelyclinical care. For example, clinical trials will be more efficient andfeasible because the event rate (future lung cancer) will be abovebackground thereby enabling the enrollment of smaller cohort sizes andreducing the numbers of patients that need to be treated to show that atherapy prevents lung cancer. As another example, knowing future risk ofcancer for subjects enables the tailoring of clinical care for thesubjects. Subjects at high risk of cancer can be monitored and/orscreened at an increased rate of frequency. Additionally, subjects ahigh risk of cancer can be informed and/or counseled to lower their risk(e.g., counseled to change lifestyle such as smoking cessation). Asanother example, since cancer treatments involve some degree of risk,knowing who is most likely to develop lung cancer will influence therisk/benefit decision analysis and reimbursement around theimplementation of therapy in the individual.

Disclosed herein is a method for predicting one or more future risks oflung cancer for a subject, the method comprising: obtaining one or moreimages captured from the subject at a single timepoint; extractingfeatures from the one or more obtained images, the extracted featurescomprising at least non-nodule specific features, wherein the non-nodulespecific features comprise one or both of lung parenchyma features orbody composition features; predicting one or more future risks of lungcancer for the subject by applying one or more trained risk predictionmodels to analyze the extracted features from the one or more obtainedimages. In various embodiments, predicting the one or more future risksof lung cancer for the subject comprises applying a M year riskprediction model to predict whether the subject is likely to developlung cancer within M years, wherein the M year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein greater than 50% of the top 3 extracted features with thehighest feature importance values are non-nodule specific features,wherein lung parenchyma features comprise one or more of percentage oflow attenuation area of the lung, percentage of high attenuation area ofthe lung, ratio of low attenuation or high attenuation area in an upperlung zone in comparison to a lower lung zone, percentage(s) of lungoccupied by any of normal tissue, centrilobular emphysema, centrilobularnodule, ground glass, honeycombing, linear scar, nodular, reticular,subpleural line, other emphysema, or cyst, and wherein body compositionfeatures comprise one or more of pectoralis cross-sectional area,pectoralis lean cross-sectional area, or sub-cutaneous fatcross-sectional area.

In various embodiments, the lung parenchyma features comprise one ormore of densitometric measures of the lung parenchyma or local histogrammeasures of the lung parenchyma. In various embodiments, thedensitometric measures of the lung parenchyma comprise one or more of:percentage of low attenuation area of the lung, percentage of highattenuation area of the lung, and ratio of low attenuation or highattenuation area in an upper lung zone in comparison to a lower lungzone. In various embodiments, the local histogram measures of the lungparenchyma comprise one or more percentage(s) of lung occupied by any ofnormal tissue, centrilobular emphysema, centrilobular nodule, groundglass, honeycombing, linear scar, nodular, reticular, subpleural line,other emphysema, or cyst. In various embodiments, body compositionfeatures comprise one or more of pectoralis cross-sectional area,pectoralis lean cross-sectional area, or sub-cutaneous fatcross-sectional area.

In various embodiments, the extracted features further comprise nodulespecific features. In various embodiments, the nodule specific featurescomprise one or more of nodule specific attenuation, nodule margindescription, nodule size, nodule shape, nodule texture, nodule diameter,Lung-RADS score, or radiomic features. In various embodiments, radiomicfeatures comprise one or more of first order statistics, 3D shape basedfeatures, 2D shape based features, gray level cooccurrence matrix, graylevel run length matrix, gray level size zone matrix, neighboring graytone difference matrix, and gray level dependence matrix. In variousembodiments, radiomic features are extracted from an image that has beentransformed by applying a filter, such as a wavelet filter or a gaussianfilter. Thus, any of first order statistics, 3D shape based features, 2Dshape based features, gray level cooccurrence matrix, gray level runlength matrix, gray level size zone matrix, neighboring gray tonedifference matrix, and gray level dependence matrix can be extractedfrom a wavelet transformed image or a gaussian transformed image. Invarious embodiments, the nodule specific features are extracted from aradiologist report. In various embodiments, the nodule specific featuresare computationally extracted by implementing an image analysisalgorithm.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 5 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 5years. In various embodiments, the 5 year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein the non-nodule specific features have higher feature importancevalues than the nodule specific features. In various embodiments,greater than 50% of the top 3 extracted features with the highestfeature importance values are non-nodule specific features. In variousembodiments, greater than 50% of the top 5 extracted features with thehighest feature importance values are non-nodule specific features. Invarious embodiments, greater than 50% of the top 10 extracted featureswith the highest feature importance values are non-nodule specificfeatures. In various embodiments, determining that the subject is acandidate comprises obtaining a classification of the subject in one ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. Invarious embodiments, the 5 year risk prediction model exhibits an areaunder the curve (AUC) value of at least 0.74. In various embodiments,the 5 year risk prediction model achieves at least a 5.1-fold increasein cumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 5 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 5 yearrisk prediction model achieves at least a 3.9-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 5year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.67. In various embodiments, the 5 year risk predictionmodel achieves at least a 3.7-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 5 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 5 year risk prediction modelachieves at least a 1.9-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 5 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.57. Invarious embodiments, the 5 year risk prediction model achieves at leasta 1.9-fold increase in cumulative incidence in comparison to abackground rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 3 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 3years. In various embodiments, the 3 year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein the non-nodule specific features have higher feature importancevalues than the nodule-specific features. In various embodiments,greater than 50% of the top 5 extracted features with the highestfeature importance values are non-nodule specific features. In variousembodiments, greater than 50% of the top 10 extracted features with thehighest feature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.79. In various embodiments, the 3 yearrisk prediction model achieves at least a 6.3-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 3 yearrisk prediction model achieves at least a 5.1-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 3year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.72. In various embodiments, the 3 year risk predictionmodel achieves at least a 5.7-fold increase in cumulative incidence incomparison to a background rate. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.676.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 3 year risk prediction modelachieves at least a 3-fold increase in cumulative incidence incomparison to a background rate. In various embodiments, determiningthat the subject is a candidate further comprises determining that thesubject is classified in Lung-RADS 1. In various embodiments, the 3 yearrisk prediction model exhibits an area under the curve (AUC) value of atleast 0.60. In various embodiments, the 3 year risk prediction modelachieves at least a 2.3-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 1 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 1year. In various embodiments, the 1 year risk prediction model comprisesnodule specific features and non-nodule specific features, wherein thenon-nodule specific features have higher feature importance values thanthe nodule-specific features. In various embodiments, determining thatthe subject is a candidate further comprises determining that thesubject is classified in any of Lung-RADS 1 or Lung-RADS 2. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 1 yearrisk prediction model achieves at least a 5.5-fold increase incumulative incidence in comparison to a background rate. In variousembodiments, determining that the subject is a candidate furthercomprises determining that the subject is classified in Lung-RADS 1. Invarious embodiments, the 1 year risk prediction model exhibits an areaunder the curve (AUC) value of at least 0.57.

In various embodiments, the 1 year risk prediction model comprisesnodule specific features and non-nodule specific features, wherein thenodule specific features have higher feature importance values than thenon-nodule specific features. In various embodiments, greater than 50%of the top 3 extracted features with the highest feature importancevalues are nodule specific features. In various embodiments, determiningthat the subject is a candidate comprises obtaining a classification ofthe subject in one of Lung-RADS 1, Lung-RADS 2, Lung-RADS 3, Lung-RADS4A, or Lung-RADS 4B. In various embodiments, the 1 year risk predictionmodel exhibits an area under the curve (AUC) value of at least 0.90. Invarious embodiments, the 1 year risk prediction model achieves at leasta 11-fold increase in cumulative incidence in comparison to a backgroundrate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.81. In various embodiments, the 1 yearrisk prediction model achieves at least a 7.6-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, greater than 50% of the top 5 extracted featureswith the highest feature importance values are nodule specific features.In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 1year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.82. In various embodiments, the 1 year risk predictionmodel achieves at least a 8.6-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying multiple risk predictionmodels to predict whether the subject is likely to develop lung cancerwithin N different time periods. In various embodiments, at least one ofthe N different time periods is any one of 6 months, 1 year, 1.5 years,2 years, 2.5 years, 3 years, 3.5 years, 4 years, 4.5 years, 5 years, 5.5years, 6 years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5 years, 9years, 9.5 years, 10 years, 10.5 years, 11 years, 11.5 years, 12 years,12.5 years, 13 years, 13.5 years, 14 years, 14.5 years, 15 years, 15.5years, 16 years, 16.5 years, 17 years, 17.5 years, 18 years, 18.5 years,19 years, 19.5 years, or 20 years. In various embodiments, N is 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different time periods.

In various embodiments, one of the one or more risk prediction models isa Lung-RADS 1-4B prediction model that is trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in any of Lung-RADS 1-4B. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1-4A model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in any of Lung-RADS 1-4A. In various embodiments,one of the one or more risk prediction models is a Lung-RADS 1-3 modelto predict the future risk of lung cancer using training images capturedfrom a training cohort of training individuals classified in any ofLung-RADS 1-3. In various embodiments, one of the one or more riskprediction models is a Lung-RADS 1-2 model trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in either Lung-RADS 1 or 2. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1 model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in Lung-RADS 1. In various embodiments, amajority of training individuals in the training cohort are previouslyclassified in Lung-RADS 1.

In various embodiments, the one or more images are computed tomography(CT) images or X-ray images. In various embodiments, the one or moreimages comprises are thoracic CT images or chest X-ray images. Invarious embodiments, the risk prediction model is trained using trainingimages of the National Lung Screening Trial (NLST). In variousembodiments, methods disclosed herein further comprise: prior topredicting one or more future risks of lung cancer for the subject:obtaining nodule-specific features corresponding to the subject;determining that the subject is a candidate for future risk predictionbased on the nodule-specific features.

In various embodiments, determining that the subject is a candidatecomprises determining that the subject does not have lung cancer or isat low-risk of developing lung cancer. In various embodiments,determining that the subject is at low-risk of developing lung cancercomprises determining that the subject does not have a nodule based onthe nodule-specific features.

In various embodiments, the lung cancer is either non-small cell lungcancer or small cell lung cancer. In various embodiments, the lungcancer is either adenocarcinoma or squamous cell carcinoma. In variousembodiments, methods disclosed herein further comprise selecting aclinical response for the subject based on the predicted future risk oflung cancer. In various embodiments, selecting a clinical response forthe subject comprises selecting an intervention for treating thesubject. In various embodiments, selecting an intervention comprisesselecting a therapeutic for administration to the subject. In variousembodiments, the selected therapeutic is prophylactically administeredto the subject to delay or prevent the development of the lung cancer.In various embodiments, the clinical response comprises providingcounseling to the subject to modify behavior of the subject. In variousembodiments, the clinical response comprises increasing a frequency offollow up for the subject. In various embodiments, the clinical responsecomprises performing or scheduling to be performed an additional riskprediction test to confirm the predicted future risk of lung cancer. Invarious embodiments, one or more of the trained risk prediction modelsare one of a random forest model or gradient boosted model.

Additionally disclosed herein is a non-transitory computer readablemedium for predicting one or more future risks of lung cancer for asubject, the non-transitory computer readable medium comprisinginstructions that, when executed by a processor, cause the processor to:obtain one or more images captured from the subject at a singletimepoint; extract features from the one or more obtained images, theextracted features comprising at least non-nodule specific features,wherein the non-nodule specific features comprise one or both of lungparenchyma features or body composition features; predict one or morefuture risks of lung cancer for the subject by applying one or moretrained risk prediction models to analyze the extracted features fromthe one or more obtained images. In various embodiments, theinstructions that cause the processor to predict the one or more futurerisks of lung cancer for the subject further comprises instructionsthat, when executed by the processor, cause the processor to apply a Myear risk prediction model to predict whether the subject is likely todevelop lung cancer within M years, wherein the M year risk predictionmodel comprises nodule specific features and non-nodule specificfeatures, wherein greater than 50% of the top 3 extracted features withthe highest feature importance values are non-nodule specific features,wherein lung parenchyma features comprise one or more of percentage oflow attenuation area of the lung, percentage of high attenuation area ofthe lung, ratio of low attenuation or high attenuation area in an upperlung zone in comparison to a lower lung zone, percentage(s) of lungoccupied by any of normal tissue, centrilobular emphysema, centrilobularnodule, ground glass, honeycombing, linear scar, nodular, reticular,subpleural line, other emphysema, or cyst, and wherein body compositionfeatures comprise one or more of pectoralis cross-sectional area,pectoralis lean cross-sectional area, or sub-cutaneous fatcross-sectional area.

In various embodiments, the lung parenchyma features comprise one ormore of densitometric measures of the lung parenchyma or local histogrammeasures of the lung parenchyma. In various embodiments, thedensitometric measures of the lung parenchyma comprise one or more of:percentage of low attenuation area of the lung, percentage of highattenuation area of the lung, and ratio of low attenuation or highattenuation area in an upper lung zone in comparison to a lower lungzone. In various embodiments, the local histogram measures of the lungparenchyma comprise one or more percentage(s) of lung occupied by any ofnormal tissue, centrilobular emphysema, centrilobular nodule, groundglass, honeycombing, linear scar, nodular, reticular, subpleural line,other emphysema, or cyst. In various embodiments, body compositionfeatures comprise one or more of pectoralis cross-sectional area,pectoralis lean cross-sectional area, or sub-cutaneous fatcross-sectional area.

In various embodiments, the extracted features further comprise nodulespecific features. In various embodiments, the nodule specific featurescomprise one or more of nodule specific attenuation, nodule margindescription, nodule size, nodule shape, nodule texture, nodule diameter,Lung-RADS score, or radiomic features. In various embodiments, radiomicfeatures comprise one or more of first order statistics, 3D shape basedfeatures, 2D shape based features, gray level cooccurrence matrix, graylevel run length matrix, gray level size zone matrix, neighboring graytone difference matrix, and gray level dependence matrix. In variousembodiments, radiomic features are extracted from an image that has beentransformed by applying a filter, such as a wavelet filter or a gaussianfilter. Thus, any of first order statistics, 3D shape based features, 2Dshape based features, gray level cooccurrence matrix, gray level runlength matrix, gray level size zone matrix, neighboring gray tonedifference matrix, and gray level dependence matrix can be extractedfrom a wavelet transformed image or a gaussian transformed image. Invarious embodiments, the nodule specific features are extracted from aradiologist report. In various embodiments, the nodule specific featuresare computationally extracted by implementing an image analysisalgorithm.

In various embodiments, the instructions that cause the processor topredict the one or more future risks of lung cancer for the subjectfurther comprises instructions that, when executed by the processor,cause the processor to apply a 5 year risk prediction model to predictwhether the subject is likely to develop lung cancer within 5 years. Invarious embodiments, the 5 year risk prediction model comprises nodulespecific features and non-nodule specific features, wherein thenon-nodule specific features have higher feature importance values thanthe nodule specific features. In various embodiments, greater than 50%of the top 3 extracted features with the highest feature importancevalues are non-nodule specific features. In various embodiments, greaterthan 50% of the top 5 extracted features with the highest featureimportance values are non-nodule specific features. In variousembodiments, greater than 50% of the top 10 extracted features with thehighest feature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 5 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.74. In various embodiments, the 5 yearrisk prediction model achieves at least a 5.1-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 5 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 5 yearrisk prediction model achieves at least a 3.9-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 5year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.67. In various embodiments, the 5 year risk predictionmodel achieves at least a 3.7-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 5 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 5 year risk prediction modelachieves at least a 1.9-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 5 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.57. Invarious embodiments, the 5 year risk prediction model achieves at leasta 1.9-fold increase in cumulative incidence in comparison to abackground rate.

In various embodiments, the instructions that cause the processor topredict the one or more future risks of lung cancer for the subjectfurther comprises instructions that, when executed by the processor,cause the processor to apply a 3 year risk prediction model to predictwhether the subject is likely to develop lung cancer within 3 years. Invarious embodiments, the 3 year risk prediction model comprises nodulespecific features and non-nodule specific features, wherein thenon-nodule specific features have higher feature importance values thanthe nodule-specific features. In various embodiments, greater than 50%of the top 5 extracted features with the highest feature importancevalues are non-nodule specific features. In various embodiments, greaterthan 50% of the top 10 extracted features with the highest featureimportance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.79. In various embodiments, the 3 yearrisk prediction model achieves at least a 6.3-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 3 yearrisk prediction model achieves at least a 5.1-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 3year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.72. In various embodiments, the 3 year risk predictionmodel achieves at least a 5.7-fold increase in cumulative incidence incomparison to a background rate. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.676.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 3 year risk prediction modelachieves at least a 3-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 3 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.60. Invarious embodiments, the 3 year risk prediction model achieves at leasta 2.3-fold increase in cumulative incidence in comparison to abackground rate.

In various embodiments, the instructions that cause the processor topredict the one or more future risks of lung cancer for the subjectfurther comprises instructions that, when executed by the processor,cause the processor to apply a 1 year risk prediction model to predictwhether the subject is likely to develop lung cancer within 1 year. Invarious embodiments, the 1 year risk prediction model comprises nodulespecific features and non-nodule specific features, wherein thenon-nodule specific features have higher feature importance values thanthe nodule-specific features. In various embodiments, determining thatthe subject is a candidate further comprises determining that thesubject is classified in any of Lung-RADS 1 or Lung-RADS 2. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 1 yearrisk prediction model achieves at least a 5.5-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 1 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.57.

In various embodiments, the 1 year risk prediction model comprisesnodule specific features and non-nodule specific features, wherein thenodule specific features have higher feature importance values than thenon-nodule specific features. In various embodiments, greater than 50%of the top 3 extracted features with the highest feature importancevalues are non-nodule specific features. In various embodiments,determining that the subject is a candidate comprises obtaining aclassification of the subject in one of Lung-RADS 1, Lung-RADS 2,Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In various embodiments, the1 year risk prediction model exhibits an area under the curve (AUC)value of at least 0.90. In various embodiments, the 1 year riskprediction model achieves at least a 11-fold increase in cumulativeincidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.81. In various embodiments, the 1 yearrisk prediction model achieves at least a 7.6-fold increase incumulative incidence in comparison to a background rate. In variousembodiments, greater than 50% of the top 5 extracted features with thehighest feature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 1year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.82. In various embodiments, the 1 year risk predictionmodel achieves at least a 8.6-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, the instructions that cause the processor topredict the one or more future risks of lung cancer for the subjectfurther comprises instructions that, when executed by the processor,cause the processor to apply multiple risk prediction models to predictwhether the subject is likely to develop lung cancer within N differenttime periods. In various embodiments, at least one of the N differenttime periods is any one of 6 months, 1 year, 1.5 years, 2 years, 2.5years, 3 years, 3.5 years, 4 years, 4.5 years, 5 years, 5.5 years, 6years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5 years, 9 years, 9.5years, 10 years, 10.5 years, 11 years, 11.5 years, 12 years, 12.5 years,13 years, 13.5 years, 14 years, 14.5 years, 15 years, 15.5 years, 16years, 16.5 years, 17 years, 17.5 years, 18 years, 18.5 years, 19 years,19.5 years, or 20 years. In various embodiments, N is 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 different timeperiods.

In various embodiments, one of the one or more risk prediction models isa Lung-RADS 1-4B prediction model that is trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in any of Lung-RADS 1-4B. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1-4A model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in any of Lung-RADS 1-4A. In various embodiments,one of the one or more risk prediction models is a Lung-RADS 1-3 modelto predict the future risk of lung cancer using training images capturedfrom a training cohort of training individuals classified in any ofLung-RADS 1-3. In various embodiments, one of the one or more riskprediction models is a Lung-RADS 1-2 model trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in either Lung-RADS 1 or 2. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1 model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in Lung-RADS 1. In various embodiments, amajority of training individuals in the training cohort are previouslyclassified in Lung-RADS 1

In various embodiments, the one or more images are computed tomography(CT) images or X-ray images. In various embodiments, the one or moreimages comprises are thoracic CT images or chest X-ray images. Invarious embodiments, the risk prediction model is trained using trainingimages of the National Lung Screening Trial (NLST).

In various embodiments, the non-transitory computer readable mediumfurther comprises instructions that, when executed by the processor,cause the processor to: prior to predicting one or more future risks oflung cancer for the subject: obtain nodule-specific featurescorresponding to the subject; determine that the subject is a candidatefor future risk prediction based on the nodule-specific features.

In various embodiments, the instructions that cause the processor todetermine that the subject is a candidate further comprises instructionsthat, when executed by the processor, cause the processor to determinethat the subject does not have lung cancer or is at low-risk ofdeveloping lung cancer. In various embodiments, the instructions thatcause the processor to determine that the subject is at low-risk ofdeveloping lung cancer further comprises instructions that, whenexecuted by the processor, cause the processor to determine that thesubject does not have a nodule based on the nodule-specific features.

In various embodiments, the lung cancer is either non-small cell lungcancer or small cell lung cancer. In various embodiments, the lungcancer is either adenocarcinoma or squamous cell carcinoma. In variousembodiments, a non-transitory computer readable medium disclosed hereinfurther comprises instructions that, when executed by the processor,cause the processor to select an intervention for treating the subjectbased on the predicted future risk of lung cancer. In variousembodiments, the instructions that cause the processor to select anintervention further comprises instructions that, when executed by theprocessor, cause the processor to select a therapeutic foradministration to the subject. In various embodiments, the selectedtherapeutic is a prophylactic for delaying or preventing the developmentof the lung cancer. In various embodiments, one or more of the trainedrisk prediction models are one of a random forest model or gradientboosted model.

Additionally disclosed herein is a system for predicting one or morefuture risks of lung cancer for a subject, the system comprising: animaging device configured to capture one or more images of the subjectat a single timepoint; and a computing device configured to perform thesteps of: obtaining one or more images captured from the subject at thesingle timepoint; extracting features from the one or more obtainedimages, the extracted features comprising at least non-nodule specificfeatures, wherein the non-nodule specific features comprise one or bothof lung parenchyma features or body composition features; predicting oneor more future risks of lung cancer for the subject by applying one ormore trained risk prediction models to analyze the extracted featuresfrom the one or more obtained images. In various embodiments, predictingthe one or more future risks of lung cancer for the subject comprisesapplying a M year risk prediction model to predict whether the subjectis likely to develop lung cancer within M years, wherein the M year riskprediction model comprises nodule specific features and non-nodulespecific features, wherein greater than 50% of the top 3 extractedfeatures with the highest feature importance values are non-nodulespecific features, wherein lung parenchyma features comprise one or moreof percentage of low attenuation area of the lung, percentage of highattenuation area of the lung, ratio of low attenuation or highattenuation area in an upper lung zone in comparison to a lower lungzone, percentage(s) of lung occupied by any of normal tissue,centrilobular emphysema, centrilobular nodule, ground glass,honeycombing, linear scar, nodular, reticular, subpleural line, otheremphysema, or cyst, and wherein body composition features comprise oneor more of pectoralis cross-sectional area, pectoralis leancross-sectional area, or sub-cutaneous fat cross-sectional area.

In various embodiments, the lung parenchyma features comprise one ormore of densitometric measures of the lung parenchyma or local histogrammeasures of the lung parenchyma. In various embodiments, thedensitometric measures of the lung parenchyma comprise one or more of:percentage of low attenuation area of the lung, percentage of highattenuation area of the lung, and ratio of low attenuation or highattenuation area in an upper lung zone in comparison to a lower lungzone. In various embodiments, the local histogram measures of the lungparenchyma comprise one or more percentage(s) of lung occupied by any ofnormal tissue, centrilobular emphysema, centrilobular nodule, groundglass, honeycombing, linear scar, nodular, reticular, subpleural line,other emphysema, or cyst. In various embodiments, body compositionfeatures comprise one or more of pectoralis cross-sectional area,pectoralis lean cross-sectional area, or sub-cutaneous fatcross-sectional area.

In various embodiments, the extracted features further comprise nodulespecific features. In various embodiments, the nodule specific featurescomprise one or more of nodule specific attenuation, nodule margindescription, nodule size, nodule shape, nodule texture, nodule diameter,Lung-RADS score, or radiomic features. In various embodiments, radiomicfeatures comprise one or more of first order statistics, 3D shape basedfeatures, 2D shape based features, gray level cooccurrence matrix, graylevel run length matrix, gray level size zone matrix, neighboring graytone difference matrix, and gray level dependence matrix. In variousembodiments, radiomic features are extracted from an image that has beentransformed by applying a filter, such as a wavelet filter or a gaussianfilter. Thus, any of first order statistics, 3D shape based features, 2Dshape based features, gray level cooccurrence matrix, gray level runlength matrix, gray level size zone matrix, neighboring gray tonedifference matrix, and gray level dependence matrix can be extractedfrom a wavelet transformed image or a gaussian transformed image. Invarious embodiments, the nodule specific features are extracted from aradiologist report. In various embodiments, the nodule specific featuresare computationally extracted by implementing an image analysisalgorithm.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 5 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 5years. In various embodiments, the 5 year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein the non-nodule specific features have higher feature importancevalues than the nodule specific features. In various embodiments,greater than 50% of the top 3 extracted features with the highestfeature importance values are non-nodule specific features. In variousembodiments, greater than 50% of the top 5 extracted features with thehighest feature importance values are non-nodule specific features. Invarious embodiments, greater than 50% of the top 10 extracted featureswith the highest feature importance values are non-nodule specificfeatures.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 5 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.74. In various embodiments, the 5 yearrisk prediction model achieves at least a 5.1-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 5 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 5 yearrisk prediction model achieves at least a 3.9-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 5year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.67. In various embodiments, the 5 year risk predictionmodel achieves at least a 3.7-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 5 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 5 year risk prediction modelachieves at least a 1.9-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 5 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.57. Invarious embodiments, the 5 year risk prediction model achieves at leasta 1.9-fold increase in cumulative incidence in comparison to abackground rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 3 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 3years. In various embodiments, the 3 year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein the non-nodule specific features have higher feature importancevalues than the nodule-specific features. In various embodiments,greater than 50% of the top 5 extracted features with the highestfeature importance values are non-nodule specific features. In variousembodiments, greater than 50% of the top 10 extracted features with thehighest feature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.79. In various embodiments, the 3 yearrisk prediction model achieves at least a 6.3-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 3 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.72. In various embodiments, the 3 yearrisk prediction model achieves at least a 5.1-fold increase incumulative incidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 3year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.72. In various embodiments, the 3 year risk predictionmodel achieves at least a 5.7-fold increase in cumulative incidence incomparison to a background rate. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.676.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 3 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.65. In various embodiments, the 3 year risk prediction modelachieves at least a 3-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified inLung-RADS 1. In various embodiments, the 3 year risk prediction modelexhibits an area under the curve (AUC) value of at least 0.60. Invarious embodiments, the 3 year risk prediction model achieves at leasta 2.3-fold increase in cumulative incidence in comparison to abackground rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying a 1 year risk prediction modelto predict whether the subject is likely to develop lung cancer within 1year. In various embodiments, the 1 year risk prediction model comprisesnodule specific features and non-nodule specific features, wherein thenon-nodule specific features have higher feature importance values thanthe nodule-specific features.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1 or Lung-RADS 2. In various embodiments, the 1 year riskprediction model exhibits an area under the curve (AUC) value of atleast 0.72. In various embodiments, the 1 year risk prediction modelachieves at least a 5.5-fold increase in cumulative incidence incomparison to a background rate. In various embodiments, determiningthat the subject is a candidate further comprises determining that thesubject is classified in Lung-RADS 1. In various embodiments, the 1 yearrisk prediction model exhibits an area under the curve (AUC) value of atleast 0.57. In various embodiments, the 1 year risk prediction modelcomprises nodule specific features and non-nodule specific features,wherein the nodule specific features have higher feature importancevalues than the non-nodule specific features. In various embodiments,greater than 50% of the top 3 extracted features with the highestfeature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatecomprises obtaining a classification of the subject in one of Lung-RADS1, Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.90. In various embodiments, the 1 yearrisk prediction model achieves at least a 11-fold increase in cumulativeincidence in comparison to a background rate.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A. In variousembodiments, the 1 year risk prediction model exhibits an area under thecurve (AUC) value of at least 0.81. In various embodiments, the 1 yearrisk prediction model achieves at least a 7.6-fold increase incumulative incidence in comparison to a background rate. In variousembodiments, greater than 50% of the top 5 extracted features with thehighest feature importance values are non-nodule specific features.

In various embodiments, determining that the subject is a candidatefurther comprises determining that the subject is classified in any ofLung-RADS 1, Lung-RADS 2, or Lung-RADS 3. In various embodiments, the 1year risk prediction model exhibits an area under the curve (AUC) valueof at least 0.82. In various embodiments, the 1 year risk predictionmodel achieves at least a 8.6-fold increase in cumulative incidence incomparison to a background rate.

In various embodiments, predicting the one or more future risks of lungcancer for the subject comprises applying multiple risk predictionmodels to predict whether the subject is likely to develop lung cancerwithin N different time periods. In various embodiments, at least one ofthe N different time periods is any one of 6 months, 1 year, 1.5 years,2 years, 2.5 years, 3 years, 3.5 years, 4 years, 4.5 years, 5 years, 5.5years, 6 years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5 years, 9years, 9.5 years, 10 years, 10.5 years, 11 years, 11.5 years, 12 years,12.5 years, 13 years, 13.5 years, 14 years, 14.5 years, 15 years, 15.5years, 16 years, 16.5 years, 17 years, 17.5 years, 18 years, 18.5 years,19 years, 19.5 years, or 20 years. In various embodiments, N is 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different time periods.

In various embodiments, one of the one or more risk prediction models isa Lung-RADS 1-4B prediction model that is trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in any of Lung-RADS 1-4B. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1-4A model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in any of Lung-RADS 1-4A. In various embodiments,one of the one or more risk prediction models is a Lung-RADS 1-3 modelto predict the future risk of lung cancer using training images capturedfrom a training cohort of training individuals classified in any ofLung-RADS 1-3. In various embodiments, one of the one or more riskprediction models is a Lung-RADS 1-2 model trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort of training individuals classified in either Lung-RADS 1 or 2. Invarious embodiments, one of the one or more risk prediction models is aLung-RADS 1 model trained to predict the future risk of lung cancerusing training images captured from a training cohort of trainingindividuals classified in Lung-RADS 1. In various embodiments, amajority of training individuals in the training cohort are previouslyclassified in Lung-RADS 1.

In various embodiments, the one or more images are computed tomography(CT) images or X-ray images. In various embodiments, the one or moreimages comprises are thoracic CT images or chest X-ray images. Invarious embodiments, the risk prediction model is trained using trainingimages of the National Lung Screening Trial (NLST).

In various embodiments, the computational device is further configuredto: prior to predicting one or more future risks of lung cancer for thesubject: obtaining nodule-specific features corresponding to thesubject; determining that the subject is a candidate for future riskprediction based on the nodule-specific features. In variousembodiments, determining that the subject is a candidate comprisesdetermining that the subject does not have lung cancer or is at low-riskof developing lung cancer. In various embodiments, determining that thesubject is at low-risk of developing lung cancer comprises determiningthat the subject does not have a nodule based on the nodule-specificfeatures.

In various embodiments, the lung cancer is either non-small cell lungcancer or small cell lung cancer. In various embodiments, the lungcancer is either adenocarcinoma or squamous cell carcinoma. In variousembodiments, the computing device is further configured to perform thestep of selecting an intervention for treating the subject based on thepredicted future risk of lung cancer. In various embodiments, selectingan intervention for treating the subject comprises selecting atherapeutic for administration to the subject. In various embodiments,the selected therapeutic is a prophylactic for delaying or preventingthe development of the lung cancer. In various embodiments, one or moreof the trained risk prediction models are one of a random forest modelor gradient boosted model.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood with regard to the followingdescription and accompanying drawings.

FIG. 1A depicts a system environment overview for determining a cancerprediction for a subject, in accordance with an embodiment.

FIG. 1B depicts a block diagram of the cancer prediction system, inaccordance with an embodiment.

FIG. 2A depicts an example flow diagram for determining a future risk ofcancer for a subject for uses such as patient enrichment, in accordancewith a first embodiment.

FIG. 2B depicts an example flow diagram for determining a future risk ofcancer for a subject for uses such as patient enrichment, in accordancewith a second embodiment.

FIG. 3 is an example flow process for determining a future risk ofcancer for a subject for uses such as patient enrichment, in accordancewith an embodiment.

FIG. 4 illustrates an example computer for implementing the entitiesshown in FIGS. 1A, 1B, 2A, 2B, and 3 .

FIG. 5A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-4B patients.

FIG. 5B depicts performance of a risk prediction model for predictinglikelihood of cancer within 3 years across Lung-RADS 1-4B patients.

FIG. 5C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across the Lung-RADS 1-4B patients.

FIG. 5D depicts the 3 year cumulative incidence function across theLung-RADS 1-4B patients.

FIG. 6A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-4A patients.

FIG. 6B depicts performance of a risk prediction model for predictinglikelihood of cancer within 3 years across Lung-RADS 1-4A patients.

FIG. 6C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-4A patients.

FIG. 6D depicts the 3 year cumulative incidence function acrossLung-RADS 1-4A patients.

FIG. 7A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-3 patients.

FIG. 7B depicts performance of a risk prediction model for predictinglikelihood of cancer within 3 years across Lung-RADS 1-3 patients.

FIG. 7C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-3 patients.

FIG. 7D depicts the 3 year cumulative incidence function acrossLung-RADS 1-3 patients.

FIG. 8A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-2 patients.

FIG. 8B depicts performance of a risk prediction model for predictinglikelihood of cancer within 3 years across Lung-RADS 1-2 patients.

FIG. 8C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-2 patients.

FIG. 8D depicts the 3 year cumulative incidence function acrossLung-RADS 1-2 patients.

FIG. 9A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1 patients.

FIG. 9B depicts performance of a risk prediction model for predictinglikelihood of cancer within 3 years across Lung-RADS 1 patients.

FIG. 9C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1 patients.

FIG. 9D depicts the 3 year cumulative incidence function acrossLung-RADS 1 patients.

FIG. 10A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-4B patients

FIG. 10B depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 3 yearsacross Lung-RADS 1-4B patients.

FIG. 11A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-4A patients

FIG. 11B depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 3 yearsacross Lung-RADS 1-4A patients.

FIG. 12A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-3 patients

FIG. 12B depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 3 yearsacross Lung-RADS 1-3 patients.

FIG. 13A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 2-4B patients

FIG. 13B depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 3 yearsacross Lung-RADS 2-4B patients.

FIG. 14A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 4A-4B patients

FIG. 14B depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 3 yearsacross Lung-RADS 4A-4B patients.

DETAILED DESCRIPTION I. Definitions

Terms used in the claims and specification are defined as set forthbelow unless otherwise specified.

The terms “subject” or “patient” are used interchangeably and encompassa cell, tissue, or organism, human or non-human, whether in vivo, exvivo, or in vitro, male or female.

The term “mammal” encompasses both humans and non-humans and includesbut is not limited to humans, non-human primates, canines, felines,murines, bovines, equines, and porcines.

The term “sample” or “test sample” can include a single cell or multiplecells or fragments of cells or an aliquot of body fluid, such as a bloodsample, taken from a subject, by means including venipuncture,excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample,scraping, surgical incision, or intervention or other means known in theart. Examples of an aliquot of body fluid include amniotic fluid,aqueous humor, bile, lymph, breast milk, interstitial fluid, blood,blood plasma, cerumen (earwax), Cowper's fluid (pre-ejaculatory fluid),chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit,tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleuralfluid, cerebrospinal fluid, synovial fluid, intracellular fluid, andvitreous humour. In various embodiments, a sample can be a biopsy of atissue, such as a lung tumor or a lung nodule.

The term “obtaining one or more images” encompasses obtaining one ormore images captured from a subject or obtaining one or more imagescaptured from a sample obtained from a subject. Obtaining one or moreimages can encompass performing steps of capturing the one or moreimages from the subject or from a sample obtained from the subject. Thephrase can also encompass receiving one or more images, e.g., from athird party that has performed the steps of capturing the one or moreimages from the subject or from a sample obtained from the subject. Theone or more images can be obtained by one of skill in the art via avariety of known ways including stored on a storage memory. In variousembodiments, “obtaining one or more images” refers to obtaining one ormore images that are each captured from a subject at a single timepoint(e.g., a single patient visit).

The term “training image” refers to an image (e.g., CT image or X-rayimage) captured from an individual that is used to train a riskprediction model, e.g., a lung cancer risk prediction model such as amodel described herein. In various embodiments, a training image is acomputed tomography (CT) image from a cohort that is built from routineclinical care of patients (e.g., from patients that were routinelyscreened). In various embodiments, a training image is a computedtomography (CT) image from a cohort built from research investigations(e.g., federally/industry sponsored research investigations). In variousembodiments, a training image is a computed tomography (CT) image fromthe National Lung Cancer Screening Trial (NLST) cohort. In variousembodiments, a training image is a computed tomography (CT) imageincluded in a custom dataset. For example, the training image can becaptured from a training individual. The term “training individual”refers to an individual from whom a training image is captured orotherwise obtained for use in training a risk prediction model.

The term “nodule specific features” refers to features of a lung nodule,examples of which include nodule specific attenuation, nodule margindescription, nodule size, nodule shape, nodule texture (e.g., smooth,spiculated, etc.), nodule diameter, and Lung-RADS score. In variousembodiments, nodule-specific features are computationally extracted fromimages (e.g., by implementing an image analysis algorithm). For example,nodule specific features can be radiomic features that are extractedusing an image processing algorithm, such as PyRadiomics. Exampleradiomic features can include first order statistics, 3D shape basedfeatures, 2D shape based features, gray level cooccurrence matrix, graylevel run length matrix, gray level size zone matrix, neighboring graytone difference matrix, and gray level dependence matrix. In variousembodiments, radiomic features are extracted from an image that has beentransformed by applying a filter, such as a wavelet filter or a gaussianfilter. Thus, any of first order statistics, 3D shape based features, 2Dshape based features, gray level cooccurrence matrix, gray level runlength matrix, gray level size zone matrix, neighboring gray tonedifference matrix, and gray level dependence matrix can be extractedfrom a wavelet transformed image or a gaussian transformed image. Asused herein, “nodule-specific features” are also referred to as“subjective features.” In various embodiments, nodule-specific featuresare extracted from a report prepared by a trained professional (e.g., aradiologist) who analyzes images of a subject, such as CT scans of thesubject.

The term “non-nodule specific features” refers to any of lung parenchymafeatures or body composition features. Non-nodule specific features caninclude features from a subject that are not nodules. Non-nodulespecific features can include features from a subject that are notassociated with nodules. Lung parenchyma features can includedensitometric measures of the lung parenchyma which may include, forexample, the percentage of the lung occupied by (i) low attenuation area(LAA), which is defined as the area/volume having an attenuation lessthan −950 Hounsfield Units (HU) and (ii) high attenuation area (HAA),which is defined as the area/volume of lung having attenuation between−600 HU and −250 HU, and the ratio between LAA in the upper lung zone tothat in the lower lung zone (Ratio LAA). Lung parenchyma features canfurther include measures of interstitial changes in the lung parenchymasuch as local histogram measures of the lung parenchyma, the percentageof lung occupied by, for example, normal tissue, centrilobularemphysema, centrilobular nodule, ground glass, honeycombing, linearscar, nodular, reticular, subpleural line, other emphysema and/or cyst.Body composition features include, for example, pectoralis majorcross-sectional area, pectoralis minor cross-sectional area, pectoralismajor lean cross-sectional area, pectoralis minor lean cross-sectionalarea, aggregate cross-sectional area of the left or right pectoralismajor or minor muscles, and subcutaneous fat cross-sectional area. Asused herein, “non-nodule specific features” are also referred to as“objective features.” In various embodiments, “non-nodule specificfeatures” are computationally extracted from images, such as imagescaptured from a subject. In various embodiments, non-nodule featuresthat are computationally extracted from features can be used toconstruct a report, such as a radiologist report that includes thenon-nodule features. In various embodiments, “non-nodule specificfeatures” do not include a duration of emphysema or a duration ofcardiovascular disease.

The phrase “Lung-RADS X-Y” is meant to include individuals classified asLung-RADS X, Lung-RADS Y, and any value in between numerical variables“X” and “Y.” For example, Lung-RADS 1-3 is indicative of individualsclassified as Lung-RADS 1, Lung-RADS 2, and Lung-RADS 3.

The phrase “Lung-RADS X-Y prediction model” refers to a risk predictionmodel that is trained using training images captured from trainingindividuals that are classified in any one of Lung-RADS X-Y.

The phrase “M year prediction model” refers to a risk prediction modelthat is trained to predict a future risk of cancer within a “M” timeperiod. In various embodiments, “M” is any of 6 months, 1 year, 1.5years, 2 years, 2.5 years, 3 years, 3.5 years, 4 years, 4.5 years, 5years, 5.5 years, 6 years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5years, 9 years, 9.5 years, 10 years, 10.5 years, 11 years, 11.5 years,12 years, 12.5 years, 13 years, 13.5 years, 14 years, 14.5 years, 15years, 15.5 years, 16 years, 16.5 years, 17 years, 17.5 years, 18 years,18.5 years, 19 years, 19.5 years, or 20 years. In various embodiments,“M” is measured from the time of acquisition of one or more images froma subject. Therefore, a 1 year prediction model refers to a riskprediction model that is trained to predict a future risk of cancerwithin 1 year from the time of acquisition of one or more images from asubject.

The phrase “M year, Lung-RADS X-Y prediction model” refers to a riskprediction model that 1) is trained using training images captured fromtraining individuals that are classified in any one of Lung-RADS X-Y and2) is trained to predict a future risk of cancer within “M” years.

The phrase “future risk of cancer” refers to a risk that subject willdevelop cancer within a given period of time, e.g., 1 year or 3 yearsfrom T0. In various embodiments, the future risk of cancer refers to alikelihood that a subject will develop cancer within a given period oftime from time zero (T0). In various embodiments, the future risk ofcancer refers to a likelihood that a subject will develop cancer within1 year. In various embodiments, the future risk of cancer refers to alikelihood that a subject will develop cancer within 3 years. In variousembodiments, the future risk of cancer refers to a likelihood that asubject will develop cancer within 5 years. In various embodiments, thefuture risk of cancer refers to a likelihood that a subject will developcancer within at least 1 year, at least 2 years, at least 3 years, atleast 4 years, at least 5 years, at least 6 years, at least 7 years, atleast 8 years, at least 9 years, at least 10 years, at least 11 years,at least 12 years, at least 13 years, at least 14 years, at least 15years, at least 16 years, at least 17 years, at least 18 years, at least19 years, or at least 20 years. In various embodiments, the “future riskof cancer” is a binary value (e.g., 0 or 1, where 0 indicates unlikelyto develop cancer in the period of time and 1 indicates likely todevelop cancer in the period of time). In various embodiments, the“future risk of cancer” is a continuous value (e.g., between 0 and 1,where a value closer to 1 indicates higher likelihood of developingcancer in the period of time).

The terms “treating,” “treatment,” or “therapy” of lung cancer shallmean slowing, stopping or reversing a cancer's progression byadministration of treatment. In some embodiments, treating lung cancermeans reversing the cancer's progression, ideally to the point ofeliminating the cancer itself. In various embodiments, “treating,”“treatment,” or “therapy” of lung cancer includes administering atherapeutic agent or pharmaceutical composition to the subject.Additionally, as used herein, “treating,” “treatment,” or “therapy” oflung cancer further includes administering a therapeutic agent orpharmaceutical composition for prophylactic purposes. Prophylaxis of acancer refers to the administration of a composition or therapeuticagent to prevent the occurrence, development, onset, progression, orrecurrence of cancer or some or all of the symptoms of lung cancer or tolessen the likelihood of the onset of lung cancer.

It must be noted that, as used in the specification, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise.

II. System Environment Overview

FIG. 1A depicts a system environment overview for determining a cancerprediction for a subject, in accordance with an embodiment. The systemenvironment 100 provides context in order to introduce a subject 110, animaging device 120, and a cancer prediction system 130 for determining acancer prediction 140 for the subject 110. Although FIG. 1A depicts onesubject 110 for whom a cancer prediction 140 is generated, in variousembodiments, the system environment 100 includes two or more subjectssuch that that cancer prediction system 130 generates cancer predictions140 for the two or more subjects (e.g., a cancer prediction for eachsubject).

In various embodiments, the subject 110 is healthy. For example, thesubject is not previously diagnosed with cancer or is not suspected ofhaving cancer. Thus, the methods for future risk prediction of cancerdescribed herein can be beneficial for early detection of cancer in thehealthy subject. In particular embodiments, the type of cancer in thesubject is a lung cancer. Thus, the methods described herein can bebeneficial for early detection of lung cancer. In various embodiments,the subject was previously diagnosed with a cancer. In such embodiments,the subject can be in remission and therefore, the methods for futurerisk prediction of cancer can be beneficial for determining whether thesubject is likely to experience a recurrence of cancer within a timeperiod.

In various embodiments, subjects predicted to develop cancer orexperience a cancer recurrence within a time period can be administeredtreatments, such as prophylactic treatments that slow or prevent theonset or recurrence of the cancer. In various embodiments, subjectspredicted to develop cancer or experience a cancer recurrence within atime period are selected to be enrolled in a clinical trial.

Referring to FIG. 1A, the imaging device 120 captures an image from thesubject 110. In various embodiments, the imaging device 120 captures animage from a test sample obtained from the subject 110. In variousembodiments, the image and/or the sample can be obtained by a thirdparty, e.g., a medical professional. Examples of medical professionalsinclude physicians, emergency medical technicians, nurses, firstresponders, psychologists, phlebotomist, medical physics personnel,nurse practitioners, surgeons, dentists, and any other obvious medicalprofessional as would be known to one skilled in the art. In variousembodiments, the image and/or the sample can be obtained in a hospitalsetting or a medical clinic. In various embodiments, the image and/orthe sample can be captured using an imaging device, such as a mobileimaging device.

In some embodiments, the imaging device 120 captures an image of ananatomical location of the subject 110. Example anatomical locations ofa subject can include lungs, thoracic cavity, kidney, liver, pancreas,brain, stomach, intestines, hip, knees, legs, arms, and face. In variousembodiments, the imaging device 120 captures an image of the thoraciccavity of the subject 110. In various embodiments, the imaging device120 captures an image of the subject's lungs. In various embodiments,the imaging device 120 captures an image of the subject's chest (e.g.,chest wall including the pectoralis muscle). In various embodiments, theimaging device 120 captures an image of the thoracic cavity includingthe subject's lungs. In various embodiments, the imaging device 120captures an image of the thoracic cavity including the subject's chest(e.g., chest wall including the pectoralis muscle). In variousembodiments, the imaging device 120 captures an image of the thoraciccavity including both the subject's lungs and the subject's chest (e.g.,chest wall including the pectoralis muscle).

In various embodiments, the imaging device 120 is one of a computedtomography (CT) scanner, magnetic resonance imaging (MRI) scanner,positron emission tomography (PET) scanner, x-ray scanner, or anultrasound imaging device. In particular embodiments, the imaging device120 is a CT scanner that captures one or more images of the subject 110.In particular embodiments, the imaging device 120 is a CT scanner thatcaptures one or more CT images of the thoracic cavity including both thesubject's lungs and the subject's chest (e.g., chest wall including thepectoralis muscle). In particular embodiments, the imaging device 120 isan X-ray scanner that captures one or more X-ray images of the chestincluding both the subject's lungs and the subject's chest (e.g., chestwall including the pectoralis muscle).

Generally, the cancer prediction system 130 analyzes one or more imagescaptured from the subject 110 (e.g., images captured by the imagingdevice 120) and generates the cancer prediction 140 for the subject 110.In various embodiments, the cancer prediction 140 determined by thecancer prediction system 130 is a predicted future risk of cancer forthe subject 110. For example, the cancer prediction 140 is a valueindicating whether the subject 110 is predicted to develop cancer withina time period (e.g., within 1 year, within 3 years, or within 5 years)from a date that the images were captured from the subject 110.

In various embodiments, to generate the cancer prediction 140, thecancer prediction system 130 extracts features from the one or moreimages and applies one or more trained risk prediction models to analyzethe features of the one or more images. A trained risk prediction modelpredicts a future risk of cancer for the subject 110 within a timeperiod. For example, the cancer prediction system 130 can apply a riskprediction model that is trained to predict a future risk of cancerwithin 3 years. In various embodiments, the cancer prediction system 130determines multiple future risks of cancer across different time periodsfor the subject 110 by applying multiple trained risk prediction models.For example, in addition to applying a risk prediction model that istrained to predict a future risk of cancer within 3 years, the cancerprediction system 130 further applies a second risk prediction modelthat is trained to predict a future risk of cancer within 5 years. Thecancer prediction system 130 can apply more trained risk predictionmodels that are trained for additional time periods (e.g., 1 year, 10years, 15 years, 20 years, etc). Generally, risk prediction models aretrained independently and not additive (e.g., cannot subtract cancerspredicted in 1 year from cancers predicted in 3 years to obtain cancerspredicted to develop 1 year but before 3 years).

In various embodiments, the cancer prediction 140 is an indicationderived from the predicted future risk of cancer for the subject, theindication identifying whether the subject 110 is to be included orexcluded from a patient cohort for enrollment in a clinical trial. Theindication is useful for clinical trial enrichment purposes. Forexample, if the subject 110 is predicted to develop cancer within a timeperiod, the indication identifies that the subject 110 is to be includedin a patient cohort for enrollment in a clinical trial. As anotherexample, if the subject 110 is not predicted to develop cancer within atime period, the indication identifies that the subject 110 is to beexcluded from the patient cohort for enrollment in a clinical trial.

In various embodiments, the cancer prediction 140 can include arecommended intervention for the subject 110 based on the predictedfuture risk of cancer. For example, if the cancer prediction system 130determines that the subject 110 is likely to develop cancer within 1year, the cancer prediction 140 can include a recommended interventionto delay or prevent the rapid onset of the cancer over the next year.

The cancer prediction system 130 can include one or more computers,embodied as a computer system 400 as discussed below with respect toFIG. 4 . Therefore, in various embodiments, the steps described inreference to the cancer prediction system 130 are performed in silico.

In various embodiments, the imaging device 120 and the cancer predictionsystem 130 are employed by different parties. For example, a first partyoperates the imaging device 120 to capture one or more images from thesubject 110 and then provides the captured one or more images to asecond party which implements the cancer prediction system 130 todetermine a cancer prediction 140. In some embodiments, the imagingdevice 120 and the cancer prediction system 130 are employed by the sameparty.

Reference is now made to FIG. 1B which depicts a block diagramillustrating the computer logic components of the cancer predictionsystem 130, in accordance with an embodiment. Here, the cancerprediction system 130 includes a feature extraction module 145, acandidate subject module 150, a risk training module 155, a riskdeployment module 160, an enrichment module 165, and a training datastore 170. In various embodiments, the cancer prediction system 130 canbe configured differently with additional or fewer modules. For example,a cancer prediction system 130 need not include the candidate subjectmodule 150. As another example, the cancer prediction system 130 neednot include the risk training module 155 or the training data store 170(as indicated by their dotted lines in FIG. 1B), and instead, the risktraining module 155 and training data store 170 are employed by adifferent system and/or party.

Generally, the feature extraction module 145 extracts features fromimages captured from subjects or training images captured from trainingindividuals. In various embodiments, the feature extraction module 145extracts non-nodule specific features from images or training images. Invarious embodiments, the feature extraction module 145 extracts nodulespecific features from images or training images. In variousembodiments, the feature extraction module 145 extracts nodule specificfeatures and non-nodule specific features from images or trainingimages. The feature extraction module 145 provides features extractedfrom training images to the risk training module 155 for training riskprediction models. In various embodiments, the feature extraction module145 provides features extracted from images captured from subjects tothe risk to the candidate subject module 150 for identifying candidatesubjects. In various embodiments, the feature extraction module 145provides features extracted from images captured from subjects to therisk deployment module 160 for deploying one or more trained riskprediction models.

The candidate subject module 150 analyzes features extracted from one ormore images captured from a subject and determines whether the subjectis a candidate subject for undergoing future risk prediction. This isuseful for identifying a subset of patients who are to undergo futurerisk prediction. For example, it may be preferable to predict futurerisk of cancer for low risk cancer patients (e.g., patients whocurrently do not have cancer and/or do not currently have a lung noduleindicative of cancer). Therefore, the candidate subject module 150 canidentify a subset of low risk cancer patients for subsequent future riskprediction. In various embodiments, the candidate subject module 150analyzes both nodule specific features and non-nodule specific featuresto determine whether a subject is a candidate subject. In variousembodiments, the candidate subject module 150 only analyzes nodulespecific features to determine whether a subject is a candidate subject.In various embodiments, the candidate subject module 150 need not beimplemented by the cancer staging system 140. For example, in a scenariowhere all subjects are to be analyzed for future risk of cancer, thenall subjects are candidate subjects who are to undergo future riskprediction.

The risk training module 155 trains risk prediction models usingtraining data derived from training individuals. For example, thetraining data includes extracted features from one or more trainingimages captured from the training individuals. In various embodiments,the risk training module 155 trains a risk prediction model comprisingboth nodule specific features and non-nodule specific features. In suchembodiments, the risk deployment module 160 implements a risk predictionmodel to analyze both nodule specific features and non-nodule specificfeatures extracted from images obtained from a subject (e.g., subject110 in FIG. 1A) to determine a future risk of cancer. In variousembodiments, the risk training module 155 trains a risk prediction modelcomprising only non-nodule specific features. In such embodiments, therisk deployment module 160 implements a risk prediction model to analyzeonly non-nodule specific features extracted from images obtained from asubject (e.g., subject 110 in FIG. 1A) to determine a future risk ofcancer.

The risk deployment module 160 implements risk prediction models toanalyze features extracted from images obtained from a subject (e.g.,subject 110 in FIG. 1A) to determine a cancer prediction, such as aprediction of future risk of cancer, for the subject 110. Training riskprediction models and deploying risk prediction models are described infurther detail below.

The enrichment module 165 selects for subjects for inclusion in apatient cohort. In various embodiments, the patient cohort is generatedfor enrollment in a clinical trial. For example, the enrichment module165 generates a patient cohort (e.g., an enriched patient cohort)including a higher percentage of patients who will develop cancer withina period of time. This enriched patient cohort enables the enrolling offewer patients in clinical trials, and/or identifies people that maybenefit from therapies that intercept and prevent lung cancer.

The components of the cancer prediction system 130 are hereafterdescribed in reference to two phases: 1) a training phase and 2) adeployment phase. More specifically, the training phase refers to thebuilding and training of one or more risk prediction models by the risktraining module 155 based on training data, such as training imagescaptured from training individuals (e.g., individuals who are known todevelop or not develop cancer within a period of time). Therefore, themodels are trained using the training data such that during thedeployment phase, implementation of the models by the risk deploymentmodule 160 enables the prediction of a future risk of cancer for asubject (e.g., subject 110 in FIG. 1A).

In some embodiments, the components of the cancer prediction system 130are applied during one of the training phase and the deployment phase.For example, the risk training module 155 and training data store 170are applied during the training phase to train a risk model.Additionally, the risk deployment module 160 is applied during thedeployment phase. In various embodiments, the components of the cancerprediction system 130 can be performed by different parties depending onwhether the components are applied during the training phase or thedeployment phase. In such scenarios, the training and deployment of theprediction model are performed by different parties. For example, therisk training module 155 and training data store 170 applied during thetraining phase can be employed by a first party (e.g., to train a riskprediction model) and the risk deployment module 160 applied during thedeployment phase can be performed by a second party (e.g., to deploy therisk prediction model).

III. Methods for Predicting Future Risk of Cancer

Embodiments described herein include methods for determining a futurerisk of cancer for a subject by applying one or more trained riskprediction models. Such methods can be performed by the cancerprediction system 130 described in FIG. 1B. Reference will further bemade to FIG. 2A, which depicts an example flow diagram 200 fordetermining a future risk of cancer for a subject for uses such aspatient enrichment, in accordance with an embodiment.

As shown in FIG. 2A, a subject image 210 captured from a subject (e.g.,subject 110 in FIG. 1A) is obtained. In various embodiments, the subjectimage 210 is a CT image captured by performing a CT scan of the subject.In various embodiments, the subject image 210 is an X-ray image capturedby performing an X-ray scan of the subject. In various embodiments, morethan one subject image 210 is captured from the subject.

In various embodiments, the feature extract module 145 extracts features215A of a subject. In various embodiments, features 215A of the subjectinclude clinical data corresponding to the subject such as age, sex,ethnicity, smoking history, geographical location, pollution exposure,and/or family history of lung cancer. In various embodiments, thefeature extraction module 145 (FIG. 1B) extracts features 215A from thesubject image 210. In various embodiments, the feature extraction module145 implements an image analysis algorithm to extract features 215A fromthe subject image 210. In various embodiments, the feature extractionmodule 145 implements an image analysis algorithm including a machinelearning model that is trained to analyze and extract features from animage. Methods for extraction radiomic features are further described inRadiomics of Lung Nodules: A multi-institutional study of robustness andagreement of quantitative imaging features. Tomography. 2016;2(4):430-437 and Radiomics: extracting more information from medicalimages using advanced feature analysis. Eur J Cancer 2012;48(4):441-446, each of which is hereby incorporated by reference in itsentirety.

In various embodiments, the feature extraction module 145 extracts atleast 2 features from subject image 210. In various embodiments, thefeature extraction module 145 extracts at least 3, at least 4, at least5, at least 6, at least 7, at least 8, at least 9, or at least 10features from subject image 210. In various embodiments, the featureextraction module 145 extracts at least 15, at least 20, at least 25, atleast 30, at least 35, at least 40, or at least 45 features from subjectimage 210. In various embodiments, the feature extraction module 145extracts at least 50 features from subject image 210. In variousembodiments, the feature extraction module 145 extracts at least 100features, at least 150 features, at least 200 features, at least 250features, at least 300 features, at least 350 features, at least 400features, at least 450 features, at least 500 features, at least 550features, at least 600 features, at least 650 features, at least 700features, at least 750 features, at least 800 features, at least 850features, at least 900 features, at least 950 features, or at least 1000features from a subject image 210. In various embodiments, the featureextraction module 145 extracts between 100 features and 1000 features.In various embodiments, the feature extraction module 145 extractsbetween 300 features and 900 features. In various embodiments, thefeature extraction module 145 extracts between 500 features and 1000features.

In various embodiments, the feature extraction module 145 extractsfeatures 215A including nodule specific features. Nodule specificfeatures refer to features of a lung nodule (e.g., a lung nodule that ispresent or absent in the subject image 210). Example nodule specificfeatures include nodule specific attenuation, nodule margin description,nodule size, nodule shape, nodule texture (e.g., smooth, spiculated,etc.), nodule diameter, and Lung-RADS score. In various embodiments,nodule specific features can be radiomic features that are extractedusing an image processing algorithm, such as PyRadiomics. Exampleradiomic features can include first order statistics, 3D shape basedfeatures, 2D shape based features, Gray level cooccurrence matrix, graylevel run length matrix, gray level size zone matrix, neighboring graytone difference matrix, and gray level dependence matrix. In variousembodiments, radiomic features are extracted from an image that has beentransformed by applying a filter, such as a wavelet filter or a gaussianfilter. Thus, any of first order statistics, 3D shape based features, 2Dshape based features, gray level cooccurrence matrix, gray level runlength matrix, gray level size zone matrix, neighboring gray tonedifference matrix, and gray level dependence matrix can be extractedfrom a wavelet transformed image or a gaussian transformed image.

In particular embodiments, the feature extraction module 145 analyzesthe subject image 210 and assigns a Lung-RADS score to the subject image210 based on one or more of the extracted nodule-specific features. Forexample, based on one or more extracted nodule specific features (e.g.,such as radiomics features), the feature extraction module 145determines that the subject image 210 does not include a lung nodule. Insuch scenarios, the feature extraction module 145 can assign the subjectimage 210 a Lung-RADS score of 1. As another example, the featureextraction module 145 analyzes the subject image 210 and determines thatthe subject image 210 includes a nodule based on one or more of theextracted nodule-specific features. Then, based on the nodule-specificfeatures, the feature extraction module 145 can assign a correspondingLung-RADS score (e.g., Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, Lung-RADS4B, or Lung-RADS 4X) according to Lung-RADS criteria, such as currentLung-RADS criteria shown in Table 1 or future Lung-RADS criteria.

In various embodiments, the feature extraction module 145 extractsfeatures 215A including non-nodule specific features. Non-nodulespecific features refer to any of lung parenchyma features (e.g.,densitometric measures of the lung parenchyma and measures ofinterstitial changes in the lung parenchyma) and body compositionmeasures of the musculature/chest wall. Densitometric measures of thelung parenchyma may include, for example, the percentage of the lungoccupied by (i) low attenuation area (LAA), which is defined as thearea/volume having an attenuation less than −950 Hounsfield Units (HU)and (ii) high attenuation area (HAA), which is defined as thearea/volume of lung having attenuation between −600 HU and −250 HU, andthe ratio between LAA in the upper lung zone to that in the lower lungzone. Measures of interstitial changes in the lung parenchyma includelocal histogram measures of the lung parenchyma, the percentage of lungoccupied by, for example, normal tissue, centrilobular emphysema,centrilobular nodule, ground glass, honeycombing, linear scar, nodular,reticular, subpleural line, other emphysema and/or cyst. Bodycomposition measures of the musculature/chest wall may include, forexample, pectoralis major cross-sectional area, pectoralis minorcross-sectional area, pectoralis major lean cross-sectional area,pectoralis minor lean cross-sectional area, aggregate cross-sectionalarea of the left or right pectoralis major or minor muscles, andsubcutaneous fat cross-sectional area.

In various embodiments, the feature extraction module 145 extractsfeatures 215A that include nodule specific features and non-nodulespecific features. In various embodiments, the feature extraction module145 extracts features 215A that include only non-nodule specificfeatures. In particular embodiments, the feature extraction module 145extracts features 215A that include only nodule specific features. Invarious embodiments, the feature extraction module 145 obtains nodulespecific features that are determined by a third party. For example, thenodule specific features may have been determined by a trainedprofessional (e.g., a radiologist) that analyzes the subject image 210.In various embodiments, the feature extraction module 145 extractsnodule specific features from a report generated by a third party. Forexample, the feature extraction module 145 extracts nodule specificfeatures from a report generated by a trained professional (e.g., aradiologist) that analyzes the subject image 210.

The candidate subject module 150 (FIG. 1B) performs subject analysis 220(shown in FIG. 2A) by analyzing the features 215A of the subject image210. Based on the analysis, the candidate subject module 150 determineswhether the subject is a candidate subject who is to undergo a futurerisk prediction. Put another way, the subject analysis 220 is ascreening process to identify candidate subjects who are eligible for afuture risk of cancer prediction. For example, subjects undergoing lungcancer screening that are determined not to have prevalent lung cancerare subsequently evaluated for their risk of developing future incidentlung cancer

As one example, the candidate subject module 150 determines that asubject is a candidate subject for undergoing future risk prediction ifthe subject's features 215A, such as clinical data of the subject, meetsone or more criterion. For example, if the subject's features 215Aindicate that the subject smokes above a threshold amount, the subjectis deemed a candidate subject for undergoing future risk prediction. Asanother example, the candidate subject module 150 determines that asubject is a candidate subject for undergoing future risk prediction ifthe subject is a low risk cancer patient (e.g., a patient who does notcurrently have lung cancer and/or does not currently have a lung noduleindicative of cancer). In various embodiments, the candidate subjectmodule 150 determines that a subject is a non-candidate subject forundergoing future risk prediction if the subject is a high risk cancerpatient (e.g., a patient currently with lung cancer and/or a patientwith a lung nodule that indicates high risk of developing lung cancer).As shown in FIG. 2A, if the subject analysis 220 determines that thesubject is a candidate subject, then the subject image 210 is taken asthe candidate subject image 225 (shown by the dotted lines) forsubsequent future risk prediction analysis. Alternatively, if thesubject analysis 220 determines that the subject is a non-candidatesubject, the patient does not further undergo future risk predictionanalysis. As an example, an intervention for non-candidate subjects 218can be provided to the subject. For example, a non-candidate subject maybe a subject already with lung cancer or with a lung nodule thatindicates that the subject has advanced lung cancer. Thus, thenon-candidate subject need undergo a future risk of cancer predictionand instead, can be provided an intervention for non-candidate subjects218 can include a cancer biopsy and/or administration of a therapeuticagent (e.g., chemotherapy, radiation) to treat the non-candidatesubject's lung cancer.

In various embodiments, the candidate subject module 150 determines thata subject is a candidate subject based on features 215A that includenodule specific features. As an example, the nodule-specific featurescan include a Lung-RADS score, such as Lung-RADS 0, Lung-RADS 1,Lung-RADS 2, Lung-RADS 3, Lung-RADS 4A, Lung-RADS 4B, or Lung-RADS 4X. Asummary of Lung-RADS score classifications and correspondingcharacteristics of lung nodules is described in Table 1. In variousembodiments, the candidate subject module 150 determines a Lung-RADSscore for the subject based on the features 215A, such as nodulespecific features. For example, the nodule specific features can includeone or more of nodule specific attenuation, nodule margin description,nodule size, nodule shape, nodule texture (e.g., smooth, spiculated,etc.), nodule diameter, Lung-RADS score, and/or radiomic features suchas first order statistics, 3D shape based features, 2D shape basedfeatures, gray level cooccurrence matrix, gray level run length matrix,gray level size zone matrix, neighboring gray tone difference matrix,and gray level dependence matrix. Thus, if the nodule attenuationfeature indicates that the lung nodule is a solid nodule and the nodulemargin and/or nodule diameter features indicate that the lung nodule is5 mm, the candidate subject module 150 can assign a Lung-RADS score of 2based on criteria specified in Table 1.

In various embodiments, the candidate subject module 150 determines thata subject is a candidate subject if the subject is assigned a Lung-RADSscore of 0. In various embodiments, the candidate subject module 150determines that a subject is a candidate subject if the subject isassigned a Lung-RADS score of 1. In various embodiments, the candidatesubject module 150 determines that a subject is a candidate subject ifthe subject is assigned a Lung-RADS score of 2. In various embodiments,the candidate subject module 150 determines that a subject is acandidate subject if the subject is assigned a Lung-RADS score of 3. Invarious embodiments, the candidate subject module 150 determines that asubject is a candidate subject if the subject is assigned a Lung-RADSscore of 4A. In various embodiments, the candidate subject module 150determines that a subject is a candidate subject if the subject isassigned a Lung-RADS score of 0 or 1. In various embodiments, thecandidate subject module 150 determines that a subject is a candidatesubject if the subject is assigned a Lung-RADS score of 0, 1, or 2. Invarious embodiments, the candidate subject module 150 determines that asubject is a candidate subject if the subject is assigned a Lung-RADSscore of 0, 1, 2, or 3. In various embodiments, the candidate subjectmodule 150 determines that a subject is a candidate subject if thesubject is assigned a Lung-RADS score of 1, 2, or 3.

In various embodiments, the candidate subject module 150 determines thata subject is a non-candidate subject if the subject is assigned aLung-RADS score of 3. In various embodiments, the candidate subjectmodule 150 determines that a subject is a non-candidate subject if thesubject is assigned a Lung-RADS score of 4A. In various embodiments, thecandidate subject module 150 determines that a subject is anon-candidate subject if the subject is assigned a Lung-RADS score of4B. In various embodiments, the candidate subject module 150 determinesthat a subject is a non-candidate subject if the subject is assigned aLung-RADS score of 4A or 4B. In various embodiments, the candidatesubject module 150 determines that a subject is a non-candidate subjectif the subject is assigned a Lung-RADS score of 3, 4A, or 4B.

In various embodiments, the candidate subject module 150 determines thata subject is a candidate subject if the nodule specific featuresindicate that the subject does not have a lung nodule. For example, thenodule specific features can include one or more of nodule attenuation,nodule margin description, or nodule diameter. Thus, if the nodulespecific features indicates that the subject image 210 does not includea nodule (e.g., lack of attenuation, lack of margins, or near-zero orzero diameter), then the candidate subject module 150 determines thatthe subject is a candidate subject.

In various embodiments, the candidate subject module 150 determines thata subject is a candidate subject even if the subject has a lung nodule.For example, if the nodule specific features indicate that the subjecthas a lung nodule, the candidate subject module 150 can further analyzethe nodule specific features to classify the nodule as a higher risknodule or a lower risk nodule. In various embodiments, the candidatesubject module 150 can classify a nodule based on whether the nodule isa solid nodule, a semi-solid nodule, or a non-solid nodule. For example,the candidate subject module 150 can classify a nodule as a higher risknodule if it is a solid nodule or a semi-solid nodule and can classify anodule as a lower risk nodule if it is a non-solid nodule. In variousembodiments, the candidate subject module 150 determines that a subjectis a candidate subject if the subject has a lower risk nodule. Thecandidate subject module 150 can determine that a subject is anon-candidate subject if the subject has a higher risk nodule.

Returning to FIG. 2A, following subject analysis 220, a subject image210 from a candidate subject is now deemed a candidate subject image225. The feature extraction module 145 extracts features 215B from thecandidate subject image 225. In various embodiments, the featureextraction module 145 extracts features 215B including one or both ofnodule specific features and non-nodule specific features from thecandidate subject image 225. In various embodiments, the featureextraction module 145 extracts features 215A that include onlynon-nodule specific features. In various embodiments, the featureextraction module 145 extracts features 215A that include only nodulespecific features. In various embodiments, the feature extraction module145 obtains nodule specific features of candidate subject image 225 thatare determined by a third party. For example, the nodule specificfeatures may have been determined by a trained professional (e.g., aradiologist) that analyzes the candidate subject image 225. In variousembodiments, the feature extraction module 145 extracts nodule specificfeatures from a report generated by a third party. For example, thefeature extraction module 145 extracts nodule specific features from areport generated by a trained professional (e.g., a radiologist) thatanalyzes the subject image 225.

In various embodiments, one or more of features 215B are the same as oneor more of features 215A. Therefore, the same features need not beextracted again and can be reused. For example, nodule specific featuresthat were previously extracted from subject image 210 as features 215Acan be the same nodule specific features that are included in features215B. In various embodiments, all of the features 215B were previouslyextracted from the subject image 210 and therefore, the previouslyextracted features can be reused here as features 215B. For example, thefeature extraction module 145 may extract features 215A and features215B prior to subject analysis 220. Thus, features 215B need not befurther extracted from candidate subject image 225 and can be reusedhere.

In various embodiments, features 215B includes additional features thatwere not included in features 215A. For example, features 215B includesnon-nodule specific features that were not previously included infeatures 215A. Thus, the feature extraction module 145 extracts theseadditional features that were not previously included in features 215A.

In various embodiments, the feature extraction module 145 extracts atleast 2 features from candidate subject image 225. In variousembodiments, the feature extraction module 145 extracts at least 3, atleast 4, at least 5, at least 6, at least 7, at least 8, at least 9, orat least 10 features from candidate subject image 225. In variousembodiments, the feature extraction module 145 extracts at least 15, atleast 20, at least 25, at least 30, at least 35, at least 40, or atleast 45 features from candidate subject image 225. In variousembodiments, the feature extraction module 145 extracts at least 50features from candidate subject image 225. In various embodiments, thefeature extraction module 145 extracts at least 100 features, at least150 features, at least 200 features, at least 250 features, at least 300features, at least 350 features, at least 400 features, at least 450features, at least 500 features, at least 550 features, at least 600features, at least 650 features, at least 700 features, at least 750features, at least 800 features, at least 850 features, at least 900features, at least 950 features, or at least 1000 features fromcandidate subject image 225. In various embodiments, the featureextraction module 145 extracts between 100 features and 1000 featuresfrom candidate subject image 225. In various embodiments, the featureextraction module 145 extracts between 300 features and 900 featuresfrom candidate subject image 225. In various embodiments, the featureextraction module 145 extracts between 500 features and 1000 featuresfrom candidate subject image 225.

The risk deployment module 160 (FIG. 1B) provides the extracted features215B to trained risk prediction models 230 (shown in FIG. 2A) togenerate a risk prediction 240. In various embodiments, as shown in FIG.2A, the risk deployment module 160 provides the extracted features 215to multiple trained risk prediction models 230 to generate multiple riskpredictions 240. In various embodiments, a risk prediction model istrained to generate a future risk of cancer prediction within a timeperiod (e.g., future risk of cancer within 1 year, within 3 years, orwithin 5 years). Therefore, to generate a prediction for multiple timeperiods, the risk deployment module 160 selects and deploys differentrisk prediction models to analyze the extracted features 215B. Forexample, the risk deployment module 160 can deploy a first riskprediction model trained to predict future risk of cancer within a firsttime period and can further deploy a second risk prediction modeltrained to predict future risk of cancer within a second time period.

In various embodiments, the risk deployment module 160 deploys 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different risk prediction models to generate future risk of cancer over1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different time periods, respectively. In various embodiments, the riskdeployment module 160 deploys 5 different risk prediction models togenerate future risk of cancer over 5 different time periods. In variousembodiments, the risk deployment module 160 deploys 3 different riskprediction models to generate future risk of cancer over 3 differenttime periods. For example, the risk deployment module 160 deploys a 1year risk prediction model, a 3 year risk prediction model, and a 5 yearrisk prediction model to generate predictions of future risk of cancerwithin 1 year, 3 years, and 5 years, respectively.

In various embodiments, each of the one or more risk prediction models230 that are deployed to analyze the features 215B were previouslytrained on training images that were separated into different regions(e.g., different lung regions). For example, a first risk predictionmodel 230 may be trained to predict presence of cancer within a firstregion of the lung, a second risk prediction model 230 may be trained topredict presence of cancer within a second region of the lung, and athird risk prediction model 230 may be trained to predict presence ofcancer within a third region of the lung. As an example, different lungregions can include the upper, middle, and lower third of the lungs byvolume or separate lobes of the lungs. Thus, the risk predictions 240generated for the subject may be future risk of cancer within particularregions (e.g., lung regions).

In various embodiments, each of the one or more risk prediction models230 that are deployed to analyze the features 215B were previouslytrained using a cohort of training individuals that aligns with thecohort of candidate subjects that were determined as a result of thesubject analysis 220. Put another way, the risk prediction models 230were previously trained using training individuals that would qualify ascandidate subjects if they were to undergo the subject analysis 220.

In various embodiments, one or more of the training individuals used totrain the risk prediction models 230 share at least one feature with thecandidate subject. As an example, if the candidate subject is classifiedas Lung-RADS 1, one or more of the training individuals used to trainthe risk prediction models 230 were also classified as Lung-RADS 1. Invarious embodiments, the risk prediction models 230 may be trained usingtraining images captured from training individuals that were classifiedas Lung-RADS 1 (referred to as a Lung-RADS 1 risk prediction model). Invarious embodiments, the risk prediction models 230 may be trained usingtraining images captured from training individuals that were classifiedas Lung-RADS 1 or Lung-RADS 2 (referred to as a Lung-RADS 1-2 riskprediction model). In various embodiments, the risk prediction models230 may be trained using training images captured from trainingindividuals that were classified as Lung-RADS 1, Lung-RADS 2, orLung-RADS 3 (referred to as a Lung-RADS 1-3 risk prediction model). Invarious embodiments, the risk prediction models 230 may be trained usingtraining images captured from training individuals that were classifiedas Lung-RADS 1, Lung-RADS 2, Lung-RADS 3, or Lung-RADS 4A (referred toas a Lung-RADS 1-4A risk prediction model). In various embodiments, therisk prediction models 230 may be trained using training images capturedfrom training individuals that were classified as Lung-RADS 1, Lung-RADS2, Lung-RADS 3, Lung-RADS 4A, or Lung-RADS 4B (referred to as aLung-RADS 1-4B risk prediction model). In various embodiments, the riskprediction models 230 may be trained using training images captured fromtraining individuals that were classified as Lung-RADS 1, Lung-RADS 2,Lung-RADS 3, Lung-RADS 4A, Lung-RADS 4B, or Lung-RADS 4X (referred to asa Lung-RADS 1-4X risk prediction model).

In a scenario in which a candidate subject is classified as Lung-RADS 1(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 1. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare any of a Lung-RADS 1 prediction model, Lung-RADS 1-2 predictionmodel, Lung-RADS 1-3 prediction model, Lung-RADS 1-4A prediction model,Lung-RADS 1-4B prediction model, or Lung-RADS 1-4X prediction model. Inparticular embodiments, the risk deployment module 160 deploys aLung-RADS 1 prediction model for a Lung-RADS 1 candidate subject. Inparticular embodiments, the risk deployment module 160 deploys aLung-RADS 1-2 prediction model for a Lung-RADS 1 candidate subject. Inparticular embodiments, the risk deployment module 160 deploys aLung-RADS 1-3 prediction model for a Lung-RADS 1 candidate subject.

In a scenario in which a candidate subject is classified as Lung-RADS 2(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 2. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare any of Lung-RADS 1-2 prediction model, Lung-RADS 1-3 predictionmodel, Lung-RADS 1-4A prediction model, Lung-RADS 1-4B prediction model,or Lung-RADS 1-4X prediction model. In particular embodiments, the riskdeployment module 160 deploys a Lung-RADS 1-2 prediction model for aLung-RADS 2 candidate subject. In particular embodiments, the riskdeployment module 160 deploys a Lung-RADS 1-3 prediction model for aLung-RADS 2 candidate subject.

In a scenario in which a candidate subject is classified as Lung-RADS 3(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 3. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare any of a Lung-RADS 1-3 prediction model, Lung-RADS 1-4A predictionmodel, Lung-RADS 1-4B prediction model, or Lung-RADS 1-4X predictionmodel.

In a scenario in which a candidate subject is classified as Lung-RADS 4A(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 4A. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare any of a Lung-RADS 1-4A prediction model, Lung-RADS 1-4B predictionmodel, or Lung-RADS 1-4X prediction model.

In a scenario in which a candidate subject is classified as Lung-RADS 4B(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 4B. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare any of a Lung-RADS 1-4B prediction model, or Lung-RADS 1-4Xprediction model.

In a scenario in which a candidate subject is classified as Lung-RADS 4X(e.g., classified either through subject analysis 220 or previouslyclassified by a third party), then the risk deployment module 160deploys one or more risk prediction models 230 that were trained usingtraining images captured from at least one training individual that wasalso previously classified as Lung-RADS 4X. Thus, the risk deploymentmodule 160 can deploy one or more risk prediction models 230 that eachare a Lung-RADS 1-4X prediction model.

In various embodiments, risk prediction models are 1) trained using acohort of training individuals that aligns with the cohort of candidatesubjects that were determined as a result of the subject analysis 220and 2) trained to generate a future risk of cancer prediction within atime period (e.g., future risk of cancer within 1 year, within 3 years,or within 5 years). For example, to generate risk predictions 240 for acandidate subject, the risk deployment module 160 applies one or morerisk prediction models 230 that 1) are trained using one or moretraining individuals that share at least one feature with the candidatesubject and 2) are trained to predict future risk of cancer withindifferent time periods.

In a scenario in which a candidate subject is classified as Lung-RADS“Z” (e.g., classified either through subject analysis 220 or previouslyclassified by a third party), the risk deployment module 160 deploys oneor more risk prediction models 230 that 1) were trained using trainingimages captured from at least one training individual that was alsopreviously classified as Lung-RADS “Z” and 2) predicts future risk ofcancer within different time periods. As used herein, these riskprediction models are referred to as a “M year, Lung-RADS X-Y predictionmodel” where “M” refers to the time period and “X-Y” refer to the rangeof Lung-RADS scores of the training individuals. For example, a 1 year,Lung-RADS 1-3 prediction model refers to a risk prediction model trainedusing training individuals previously classified in Lung-RADS 1-3, andtrained to predict a future risk of cancer within 1 year.

In various embodiments, for a candidate subject classified as Lung-RADS1, the risk deployment module 160 deploys one or more M year, Lung-RADSX-Y prediction models where the “M” is variable, but the “X” and “Y” arefixed. For example, for a candidate subject classified as Lung-RADS 1,“M” can range from 1-5 years, whereas X=1 and Y=any value from 1 to 4B.As one example, Y=3 and therefore, the risk deployment module 160 candeploy a 1 year, Lung-RADS 1-3 prediction model, a 2 year, Lung-RADS 1-3prediction model, a 3 year, Lung-RADS 1-3 prediction model, a 4 year,Lung-RADS 1-3 prediction model, and a 5 year, Lung-RADS 1-3 predictionmodel. In other embodiments, the risk deployment module 160 can deployadditional risk prediction models than described here (e.g., range of Mis 1-10, 1-15, or 1-20 years e.g., X and Y are differently selected toprovide different ranges of Lung-RADS scores).

In particular embodiments where a candidate subject is classified asLung-RADS 1, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-3 prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-3 prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-3 prediction model).

In particular embodiments where a candidate subject is classified asLung-RADS 2, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-3 prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-3 prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-3 prediction model).

In particular embodiments where a candidate subject is classified asLung-RADS 3, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-3 prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-3 prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-3 prediction model).

In particular embodiments where a candidate subject is classified asLung-RADS 4A, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-4A prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-4A prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-4A prediction model).

In particular embodiments where a candidate subject is classified asLung-RADS 4B, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-4B prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-4B prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-4B prediction model).

In particular embodiments where a candidate subject is classified asLung-RADS 4X, the risk deployment module 160 deploys 1) a first riskprediction model 230 that predicts a future risk of cancer within 1 year(e.g., a 1 year, Lung-RADS 1-4X prediction model), 2) a second riskprediction model 230 that predicts a future of cancer within 3 years(e.g., a 3 year, Lung-RADS 1-4X prediction model), and 3) a third riskprediction model 230 that predicts a future of cancer within 5 years(e.g., a 5 year, Lung-RADS 1-4X prediction model).

As shown in FIG. 2A, the risk predictions 240 can be used for patientenrichment 250. For example, the subject can be included in one or morepatient cohorts that are to be enrolled in a clinical study. Methods forpatient enrichment are described in further detail below.

Reference is now made to FIG. 2B, which depicts an example flow diagramfor determining a future risk of cancer for a subject for uses such aspatient enrichment, in accordance with a second embodiment. Here, FIG.2B differs from FIG. 2A in that FIG. 2B does not include a subjectanalysis step (e.g., step 220 shown in FIG. 2A). Thus, FIG. 2B depictsan embodiment where subjects do not undergo a screening. Instead, allsubjects are candidate subjects who subsequently undergo future riskprediction. In various embodiments, subjects either have or do not havea lung nodule. In various embodiments, subjects can be previouslyclassified in any one of Lung-RADS 1, Lung-RADS 2, Lung-RADS 3,Lung-RADS 4A, Lung-RADS 4B, or Lung-RADS 4X. Thus, in such embodiments,all subjects, regardless of their lung nodule staging and/or Lung-RADSscore, undergo future risk prediction.

As shown in FIG. 2B, the candidate subject image 255 undergoes featureextraction to obtain features 260. In various embodiments, features 260include one or both of nodule specific features and non-nodule specificfeatures of the candidate subject image 225. In various embodiments,features 260 include only non-nodule specific features. In variousembodiments, features 260 include only nodule specific features. Invarious embodiments, the feature extraction module 140 extracts nodulespecific features by implementing an image analysis algorithm, such asan image analysis algorithm that involves implementing a trained machinelearning model. In various embodiments, the feature extraction module140 extracts nodule specific features by implementing PyRadiomics.PyRadiomics is described in further detail in “Computational radiomicssystem to decode the radiographic phenotype.” Cancer Research;77(21):e104-e107, which is hereby incorporated by reference in itsentirety.

In various embodiments, nodule specific features of candidate subjectimage 255 are determined by a third party. For example, the nodulespecific features may have been determined by a trained professional(e.g., a radiologist) that analyzes the candidate subject image 255. Invarious embodiments, the feature extraction module 145 extracts nodulespecific features from a report generated by a third party. For example,the feature extraction module 145 extracts nodule specific features froma report generated by a trained professional (e.g., a radiologist) thatanalyzes the subject image 225.

The risk deployment module 260 applies one or more risk predictionmodels 265 to analyze the features 260 to generate the risk prediction270. Thus, the risk prediction 270 can be used for patient enrichment275. In various embodiments, the deployment of the risk predictionmodels 265 to generate the risk prediction 270 as shown in FIG. 2B isthe same process as deploying risk prediction models 230 to generaterisk predictions 240 as shown in FIG. 2A.

Here, the one or more risk prediction models 265 are trained on trainingimages obtained from training individuals that span the full range ofpossible subjects. For example, the risk prediction models 265 aretrained using training images captured from training individuals eitherhaving a lung nodule or not having a lung nodule. As another example,the risk prediction models 265 are trained using training imagescaptured from training individuals of any of Lung-RADS 1, Lung-RADS 2,Lung-RADS 3, Lung-RADS 4A, Lung-RADS 4B, or Lung-RADS 4X (e.g.,Lung-RADS 1-4X risk prediction models).

In various embodiments, the risk deployment module 260 deploys multiplefuture risk models 265 to predict multiple risk predictions 270. Forexample, the risk deployment module 260 deploys multiple M year,Lung-RADS 1-4X risk prediction models, where “M” refers to the timeperiod in which cancer risk is being evaluated. In various embodiments,“M” is at least 1 year, at least 2 years, at least 3 years, at least 4years, at least 5 years, at least 6 years, at least 7 years, at least 8years, at least 9 years, at least 10 years, at least 11 years, at least12 years, at least 13 years, at least 14 years, at least 15 years, atleast 16 years, at least 17 years, at least 18 years, at least 19 years,and/or at least 20 years. In particular embodiments, the risk deploymentmodule 260 deploys 1) a 1 year, Lung-RADS 1-4X risk prediction model, 2)a 3 year, Lung-RADS 1-4X risk prediction model, and 3) a 5 year,Lung-RADS 1-4X risk prediction model, thereby generating future risk ofcancer predictions for 1 year, 3 year, and 5 year time periods.

In various embodiments, upon being deployed, a risk prediction modelanalyzes the extracted image features and generates a predicted scorethat can be indicative of whether the subject is likely to developcancer within a time period. For example, the risk prediction model canbe a regression model (e.g., a logistic regression or linear regressionmodel) that calculates a predicted score by combining a set of trainedparameters with values of the extracted image features. As anotherexample, the risk prediction model can be a neural network model thatcalculates a predicted score by combining a set of trained parametersassociated with nodes and layers of the neural network with values ofthe extracted image features. As another example, the risk predictionmodel can be a random forest model that calculates a predicted score bycombining a set of trained parameters associated with decision treenodes with values of the extracted image features. As another example,the risk prediction model can be a gradient boosted machine model thatcalculates a predicted score by combining a set of trained parametersassociated with decision tree nodes with values of the extracted imagefeatures.

In various embodiments, the risk prediction model compares the predictedscore to one or more reference scores. In various embodiments, the oneor more reference scores are threshold cutoff values. For example, athreshold cutoff value can be between 0 and 1, such as 0.1, 0.2, 0.3,0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. In particular embodiments, a thresholdvalue is 0.1. In particular embodiments, a threshold value is 0.3.Therefore, if the predicted score is above the threshold referencescore, the subject is classified into one category (e.g., likely todevelop cancer). If the predicted score is below the threshold referencescore, the subject is classified into a different category (e.g.,unlikely to develop cancer).

In various embodiments, multiple reference threshold scores can beimplemented to create multiple classification groups. For example, afirst threshold value is 0.1 and a second threshold value is 0.3.Therefore, if the predicted score is below the first threshold value,the subject is classified into a first category (e.g., unlikely todevelop cancer. If the predicted score is between the first and secondthreshold values, the subject is classified into a second category(e.g., low risk of developing cancer). If the predicted score is greaterthan the second threshold value, the subject is classified into a thirdcategory (e.g., high risk of developing cancer).

As one example, a reference score corresponds to one or more trainingindividuals. For example, a reference score can correspond to trainingindividuals that were known to develop cancer within the time period. Asanother example, a reference score can correspond to trainingindividuals that were known to not develop cancer within the timeperiod. Thus, if the predicted score for the subject is notsignificantly different (e.g., p-value>0.05) in comparison to thereference score corresponding to training individuals that were known todevelop cancer within the time period, then the risk prediction modelcan classify the subject as likely to develop cancer within the timeperiod. If the predicted score for the subject is significantlydifferent (e.g., p-value<0.05) in comparison to the reference scorecorresponding to training individuals that were known to develop cancerwithin the time period, then the risk prediction model can classify thesubject as not likely to develop cancer within the time period. If thepredicted score for the subject is not significantly different (e.g.,p-value>0.05) in comparison to the reference score corresponding totraining individuals that were known to not develop cancer within thetime period, then the risk prediction model can classify the subject asnot likely to develop cancer within the time period. If the predictedscore for the subject is significantly different (e.g., p-value<0.05) incomparison to the reference score corresponding to training individualsthat were known to not develop cancer within the time period, then therisk prediction model can classify the subject as likely to developcancer within the time period.

In various embodiments, during training, a risk of future cancerthreshold is defined that demarcates high from low risk subjects. Then,only the high risk subjects are included in any given model. In variousembodiments, each risk prediction model has a unique future cancerthreshold used to demarcate subjects into high or low risk. Once thosethresholds are defined for each risk prediction model, the riskprediction model is deployed for a subject to predict a future risk ofcancer. If that risk is above the threshold defined in training, thesubject can be classified as having a high future risk of cancer.

As shown in FIG. 2B, the risk predictions 270 can be used for patientenrichment 275. For example, the subject can be included in one or morepatient cohorts that are to be enrolled in a clinical. Methods forpatient enrichment are described in further detail below.

In various embodiments, the risk predictions 270 for the subject can bedisplayed to a user e.g., a clinician user. Thus, the clinician user caninform the subject of the future risk of cancer that is predicted forthe subject. In various embodiments, additional/other information can bedisplayed to a user e.g., a clinician user. For example, if a futurerisk of cancer prediction for a subject indicates that the subject islikely to develop cancer within a time period, information such as thefeatures that most heavily contributed to the future risk of cancerprediction can be displayed to the user e.g., clinician user. Forexample, a subject predicted to have a future risk of cancer can belargely due to a percentage of the subject's lung occupied bycentrilobular emphysema. Thus, the identification of the feature and/orthe value of the feature (e.g., percentage of the subject's lungoccupied by centrilobular emphysema) can be displayed to a user e.g.,clinician user. In various embodiments, the top 1, top 2, top 3, top 4,top 5, top 6, top 7, top 8, top 9, or top 10 features that most heavilycontributed to the future risk of cancer prediction for the subject canbe displayed to a user e.g., clinician user. The display of the heavilycontributing features can provide context to the clinician user inunderstanding the features that resulted in the future risk of cancerprediction.

IV. Methods of Patient Enrichment

Generally, future risk of cancer predictions from the risk predictionmodels are used for patient enrichment. For example, the future risk ofcancer predictions provide insight as to whether a subject is likely todevelop cancer within time periods (e.g., within 1 year, within 3 years,or within 5 years). Thus, for subjects that are predicted to developcancer within a particular time period, the subjects can be selected forinclusion in a patient cohort that is to be enrolled in a clinicaltrial. Given the insight provided by the future risk of cancerpredictions, this enables the enrollment of fewer subjects in patientcohorts for clinical trials. Thus, fewer resources are needed conductingthe clinical trial and tracking the subjects in the patient cohort.Additionally, subjects that are not included in the patient cohort(e.g., subjects that are predicted to not develop cancer within a timeperiod) can be used for other purposes (e.g., enrolled in other clinicaltrials).

In various embodiments, a subject is selected for inclusion in a patientcohort based on one or more of the multiple risk predictions generatedfor the patient. For example, the subject may have a first riskprediction indicating that the subject will not develop cancer within 1year, will not develop cancer within 3 years, but is likely to developcancer within 5 years. Therefore, the subject is selected for inclusionin a patient cohort for enrollment in a cancer clinical trial involvingadministration of a prophylactic therapeutic agent.

As another example, the subject may have a first risk predictionindicating that the subject is likely to develop cancer within 1 yearand therefore, is also likely to develop cancer within 3 years and 5years. Thus, the subject is selected for inclusion in a patient cohortfor enrollment in a cancer clinical trial involving aggressive cancertreatment (e.g., tumor resection and/or administration of therapeuticagent).

In various embodiments, the patient enrichment process using the riskpredictions for a plurality of subjects generates an enriched cohort ofpatients that are more likely to develop cancer in comparison to arandomly generated patient cohort. In various embodiments, the patientenrichment process generates an enriched cohort of patients thatexperiences at least a 1.5-fold increase in cumulative cancer incidencein comparison to a randomly generated patient cohort. In variousembodiments, the patient enrichment process generates an enriched cohortof patients that experiences at least a 1.6-fold increase, at least a1.7-fold increase, at least a 1.8-fold increase, at least a 1.9-foldincrease, at least a 2-fold increase, at least a 3-fold increase, atleast a 4-fold increase, at least a 5-fold increase, at least a 6-foldincrease, at least a 7-fold increase, at least a 8-fold increase, atleast a 9-fold increase, at least a 10-fold increase, at least a 11-foldincrease, at least a 12-fold increase, at least a 13-fold increase, atleast a 14-fold increase, at least a 15-fold increase, at least a16-fold increase, at least a 17-fold increase, at least a 18-foldincrease, at least a 19-fold increase, at least a 20-fold increase, atleast a 21-fold increase, at least a 22-fold increase, at least a23-fold increase, at least a 24-fold increase, at least a 25-foldincrease, at least a 26-fold increase, at least a 27-fold increase, atleast a 28-fold increase, at least a 29-fold increase, or at least a30-fold increase in cumulative cancer incidence in comparison to arandomly generated patient cohort.

V. Training a Risk Prediction Model

Generally, a risk prediction model is structured such that it analyzesfeatures extracted from an image, such as non-nodule specific featuresand/or nodule specific features, and predicts a future cancer risk forthe subject based on the extracted features. In various embodiments, therisk prediction model is any one of a regression model (e.g., linearregression, logistic regression, or polynomial regression), decisiontree, random forest, gradient boosted machine learning model, supportvector machine, Naïve Bayes model, k-means cluster, or neural network(e.g., feed-forward networks, convolutional neural networks (CNN), deepneural networks (DNN), autoencoder neural networks, generativeadversarial networks, or recurrent networks (e.g., long short-termmemory networks (LSTM), bi-directional recurrent networks, deepbi-directional recurrent networks), or any combination thereof. Inparticular embodiments, the risk prediction model is a logisticregression model. In particular embodiments, the risk prediction modelis a random forest classifier. In particular embodiments, the riskprediction model is a gradient boosting model.

The risk prediction model can be trained using a machine learningimplemented method, such as any one of a linear regression algorithm,logistic regression algorithm, decision tree algorithm, support vectormachine classification, Naïve Bayes classification, K-Nearest Neighborclassification, random forest algorithm, deep learning algorithm,gradient boosting algorithm, and dimensionality reduction techniquessuch as manifold learning, principal component analysis, factoranalysis, autoencoder regularization, and independent componentanalysis, or combinations thereof. In particular embodiments, themachine learning implemented method is a logistic regression algorithm.In particular embodiments, the machine learning implemented method is arandom forest algorithm. In particular embodiments, the machine learningimplemented method is a gradient boosting algorithm, such as XGboost. Invarious embodiments, the risk prediction model is trained usingsupervised learning algorithms, unsupervised learning algorithms,semi-supervised learning algorithms (e.g., partial supervision), weaksupervision, transfer, multi-task learning, or any combination thereof.

In various embodiments, the risk prediction model has one or moreparameters, such as hyperparameters or model parameters. Hyperparametersare generally established prior to training. Examples of hyperparametersinclude the learning rate, depth or leaves of a decision tree, number ofhidden layers in a deep neural network, number of clusters in a k-meanscluster, penalty in a regression model, and a regularization parameterassociated with a cost function. Model parameters are generally adjustedduring training. Examples of model parameters include weights associatedwith nodes in layers of neural network, support vectors in a supportvector machine, node values in a decision tree, and coefficients in aregression model. The model parameters of the risk prediction model aretrained (e.g., adjusted) using the training data to improve thepredictive capacity of the risk prediction model.

The risk training module 155 trains the risk prediction model usingtraining data. The training data can be stored and/or retrieved fromtraining data store 170. In various embodiments, the training dataincludes extracted features from training images obtained from trainingindividuals (e.g., individuals that are known to develop or not developcancer within a period of time). In various embodiments, the trainingdata can be obtained from a split of a dataset. For example, the datasetcan undergo a 50:50 training:testing dataset split. In some embodiments,the dataset can undergo a 60:40 training:testing dataset split. In someembodiments, the dataset can undergo a 80:20 training:testing datasetsplit.

In various embodiments, the training data used for training theimputation model includes reference ground truths that indicate that atraining individual developed cancer within a time period (hereafteralso referred to as “positive” or “+”) or whether the trainingindividual did not develop cancer within the time period (hereafter alsoreferred to as “negative” or “−”). In various embodiments, the referenceground truths in the training data are binary values, such as “1” or“0.” For example, a training individual that developed cancer within atime period can be identified in the training data with a value of “1”whereas a training individual that did not develop cancer within thetime period can be identified in the training data with a value of “0.”In various embodiments, the risk training module 155 trains the riskprediction model using the training data to minimize a loss functionsuch that the risk prediction model can better predict the outcome(e.g., future presence or absence of cancer within a time period) basedon the input (e.g., extracted features of the training image). Invarious embodiments, the loss function is constructed for any of a leastabsolute shrinkage and selection operator (LASSO) regression, Ridgeregression, or ElasticNet regression. In various embodiments, the riskprediction model is a random forest model, and is trained to minimizeone of Gini impurity or Entropy metrics for feature splitting, therebyenabling the risk prediction model to more accurately predict futurecancer risk.

In various embodiments, the training data can be obtained and/or derivedfrom a publicly available database. For example, the training data canbe obtained and/or derived from the National Lung Screening Trial(NLST). In some embodiments, the training data can be obtained andcollected independent of publicly available databases e.g., by capturingimages from a plurality of training individuals. Such training data canbe a custom dataset.

In various embodiments, a risk prediction model is trained using aspecific cohort of training individuals. In various embodiments, therisk prediction model is trained using a cohort of training individualsthat do not have lung nodules. In various embodiments, the riskprediction model is trained using a cohort of training individuals thathave lung nodules. In various embodiments, the risk prediction model isa Lung-RADS 1 prediction model that is trained using a cohort oftraining individuals that are previously classified in Lung-RADS 1. Invarious embodiments, the risk prediction model is a Lung-RADS 2prediction model that is trained using a cohort of training individualsthat are previously classified in any one of Lung-RADS 1-2. In variousembodiments, the risk prediction model is a Lung-RADS 1-3 predictionmodel that is trained using a cohort of training individuals that arepreviously classified in any one of Lung-RADS 1-3. In variousembodiments, the risk prediction model is a Lung-RADS 1-4A predictionmodel that is trained using a cohort of training individuals that arepreviously classified in any one of Lung-RADS 1-4A. In variousembodiments, the risk prediction model is a Lung-RADS 1-4B predictionmodel that is trained using a cohort of training individuals that arepreviously classified in any one of Lung-RADS 1-4B. In variousembodiments, the risk prediction model is a Lung-RADS 1-4X predictionmodel that is trained using a cohort of training individuals that arepreviously classified in any one of Lung-RADS 1-4X.

In various embodiments, for each of any of the Lung-RADS 1 predictionmodel, Lung-RADS 1-2 prediction model, Lung-RADS 1-3 prediction model,Lung-RADS 1-4A prediction model, Lung-RADS 1-4B prediction model, orLung-RADS 1-4X prediction model, the risk prediction model is trainedusing a training cohort of training individuals, where a majority (e.g.,greater than 50%) of training individuals in the training cohort werepreviously classified as Lung-RADS 1. In various embodiments, for eachof any of the Lung-RADS 1 prediction model, Lung-RADS 1-2 predictionmodel, Lung-RADS 1-3 prediction model, Lung-RADS 1-4A prediction model,Lung-RADS 1-4B prediction model, or Lung-RADS 1-4X prediction model, therisk prediction model is trained using a training cohort of trainingindividuals, where a majority (e.g., greater than 50%) of trainingindividuals in the training cohort were previously classified asLung-RADS 2. In various embodiments, for each of any of the Lung-RADS 1prediction model, Lung-RADS 1-2 prediction model, Lung-RADS 1-3prediction model, Lung-RADS 1-4A prediction model, Lung-RADS 1-4Bprediction model, or Lung-RADS 1-4X prediction model, the riskprediction model is trained using a training cohort of trainingindividuals, where a majority (e.g., greater than 50%) of trainingindividuals in the training cohort were previously classified asLung-RADS 1 or Lung-RADS 2. As shown in Table 1, ˜90% of individualslikely fall in Lung-RADS 1 or Lung-RADS 2. Therefore, such individualscan be used to train any of the risk prediction models.

In various embodiments, a risk prediction model is a “M” year predictionmodel trained to predict a future risk of cancer within “M” years, suchas within at least 1 year, at least 2 years, at least 3 years, at least4 years, at least 5 years, at least 6 years, at least 7 years, at least8 years, at least 9 years, at least 10 years, at least 11 years, atleast 12 years, at least 13 years, at least 14 years, at least 15 years,at least 16 years, at least 17 years, at least 18 years, at least 19years, or at least 20 years. Thus, the training data used for trainingthe imputation model includes reference ground truths that indicate thata training individual developed cancer within “M” years.

In various embodiments, a risk prediction model 1) is trained using aspecific cohort of training individuals and 2) is trained to predict afuture risk of cancer within “M” years. As described above, the cohortof training individuals may be previously classified within a range ofLung-RADS scores (e.g., Lung-RADS X-Y). Thus, a risk prediction modelmay be a M year, Lung-RADS X-Y prediction model that 1) is trained usinga cohort of training individuals classified in Lung-RADS X-Y and 2) istrained to predict a future risk of cancer with “M” years.

In various embodiments, a risk prediction model includes both non-nodulespecific features and nodule specific features. Therefore, in trainingthe risk prediction model, the risk prediction model analyzes bothnon-nodule specific features and nodule specific features extracted froma training image and attempts to generate a prediction that minimizes aloss function. Generally, features of the risk prediction model haveimportance values that reflect how heavily each feature influences theprediction generated by the risk prediction model. For example, a higherimportance value for a feature indicates that the feature more heavilyinfluences the prediction generated by the risk prediction model incomparison to a different feature with a lower importance value.

In various embodiments, the nodule specific features of the riskprediction model more heavily influence the prediction of future risk ofcancer in comparison to the non-nodule specific features. For example,the nodule specific features of the risk prediction model have higherfeature importance values than the non-nodule specific features of therisk prediction model. Generally, nodule specific features have higherimportance values for risk prediction models that are trained to predictrisk of cancer within shorter time periods (e.g., 1 year as opposed to 3years or 5 years). Additionally, nodule specific features have higherimportance values for risk prediction models that are trained usinghigher risk lung cancer patients (e.g., patients that are classified asLung-RADS 4A or Lung-RADS 4B).

In various embodiments, the feature with the highest importance value ofthe risk prediction model is a nodule specific feature. In variousembodiments, the top 2 features with the highest importance value of therisk prediction model are nodule specific features. In variousembodiments, the top 3 features with the highest importance value of therisk prediction model are nodule specific features. In variousembodiments, the top 4 features with the highest importance value of therisk prediction model are nodule specific features. In variousembodiments, the top 5, 6, 7, 8, 9, or 10 features with the highestimportance value of the risk prediction model are nodule specificfeatures.

In various embodiments, greater than 50% of the top 3 features with thehighest feature importance values are nodule specific features. Invarious embodiments, 2 of the top 3 features with the highest featureimportance values are nodule specific features. In various embodiments,3 of the top 5 features with the highest feature importance values arenodule specific features. In various embodiments, 4 of the top 5features with the highest feature importance values are nodule specificfeatures. In various embodiments, greater than 50% of the top 5 featureswith the highest feature importance values are nodule specific features.In various embodiments, greater than 50% of the top 10 features with thehighest feature importance values are nodule specific features. Invarious embodiments, 6, 7, 8, 9, or 10 of the top 10 features with thehighest feature importance values are nodule specific features.

In various embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 1 year risk prediction model(e.g., predicts risk of developing cancer within 1 year). In variousembodiments, such a risk prediction model is a model trained on trainingimages that include high risk lung nodules. In various embodiments, sucha risk prediction model can be trained on training images derived fromindividuals classified in Lung-RADS 4A. In various embodiments, such arisk prediction model can be trained on training images derived fromindividuals classified in Lung-RADS 4B. In various embodiments, such arisk prediction model can be trained on training images derived fromindividuals classified in Lung-RADS 4X. In various embodiments, such arisk prediction model can be trained on training images derived fromindividuals classified in Lung-RADS 4A, Lung-RADS 4B, Lung-RADS 4X, orany combination thereof. For example, such a risk prediction model canbe any one of a Lung-RADS 1-4A, Lung-RADS 1-4B, or Lung-RADS 1-4Xprediction model.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 1 year, Lung-RADS 1-4Bprediction model. For example, as shown in Table 3, the top threefeatures in terms of feature importance for a 1 year, Lung-RADS 1-4Bprediction model are nodule specific features. Additionally, 4 of thetop 5 features in terms of feature importance for a 1 year, Lung-RADS1-4B prediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 3 year, Lung-RADS 1-4Bprediction model. For example, as shown in Table 3, 2 of the top 3features in terms of feature importance for a 3 year, Lung-RADS 1-4Bprediction model are nodule specific features. Additionally, 3 of thetop 5 features in terms of feature importance for a 3 year, Lung-RADS1-4B prediction model are nodule specific features. As another example,as shown in Table 19, the top 3 features in terms of feature importancefor a 3 year, Lung-RADS 1-4B prediction model are nodule specificfeatures. Additionally, 4 of the top 5 features in terms of featureimportance for a 3 year, Lung-RADS 1-4B prediction model are nodulespecific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 5 year, Lung-RADS 1-4Bprediction model. For example, as shown in Table 3, 2 of the top 3features in terms of feature importance for a 5 year, Lung-RADS 1-4Bprediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 1 year, Lung-RADS 1-4Aprediction model. For example, as shown in Table 6, the top 3 featuresin terms of feature importance for a 1 year, Lung-RADS 1-4A predictionmodel are nodule specific features. Additionally, 4 of the top 5features in terms of feature importance for a 1 year, Lung-RADS 1-4Aprediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 3 year, Lung-RADS 1-4Aprediction model. For example, as shown in Table 6, the top 3 featuresin terms of feature importance for a 3 year, Lung-RADS 1-4A predictionmodel are nodule specific features. Additionally, 4 of the top 5features in terms of feature importance for a 3 year, Lung-RADS 1-4Aprediction model are nodule specific features. As another example, asshown in Table 20, the top 3 features in terms of feature importance fora 3 year, Lung-RADS 1-4A prediction model are nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance fora 3 year, Lung-RADS 1-4A prediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 5 year, Lung-RADS 1-4Aprediction model. For example, as shown in Table 6, 2 of the top 3features in terms of feature importance for a 5 year, Lung-RADS 1-4Aprediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 1 year, Lung-RADS 1-3prediction model. For example, as shown in Table 9, 2 of the top 3features in terms of feature importance for a 1 year, Lung-RADS 1-3prediction model are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 3 year, Lung-RADS 2-4Bprediction model. For example, as shown in Table 22, the top 3 featuresin terms of feature importance for a 3 year, Lung-RADS 2-4B predictionmodel are nodule specific features. Additionally, the top 5 features interms of feature importance for a 3 year, Lung-RADS 2-4B predictionmodel are nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where nodule specific features of the risk predictionmodel have higher feature importance values than the non-nodule specificfeatures of the risk prediction model) is a 3 year, Lung-RADS 4A-4Bprediction model. For example, as shown in Table 23, the top 3 featuresin terms of feature importance for a 3 year, Lung-RADS 4A-4B predictionmodel are nodule specific features. Additionally, the top 5 features interms of feature importance for a 3 year, Lung-RADS 4A-4B predictionmodel are nodule specific features.

In various embodiments, the non-nodule specific features of the riskprediction model more heavily influence the prediction of future risk ofcancer in comparison to the nodule specific features. For example, thenon-nodule specific features of the risk prediction model have higherfeature importance values than the nodule specific features of the riskprediction model. Generally, non-nodule specific features have higherimportance values for risk prediction models that are trained to predictrisk of cancer within longer time periods (e.g., 3 or 5 years as opposedto 1 year). Additionally, non-nodule specific features have higherimportance values for risk prediction models that are trained usingpatients that are at lower risk of lung cancer (e.g., patients that areclassified as Lung-RADS 2 or 3) or patients that do not yet have anodule (e.g., Lung-RADS 1).

In various embodiments, the feature with the highest importance value ofthe risk prediction model is a non-nodule specific feature. In variousembodiments, the top 2 features with the highest importance value of therisk prediction model are non-nodule specific features. In variousembodiments, the top 3 features with the highest importance value of therisk prediction model are non-nodule specific features. In variousembodiments, the top 4 features with the highest importance value of therisk prediction model are non-nodule specific features. In variousembodiments, the top 5, 6, 7, 8, 9, or 10 features with the highestimportance value of the risk prediction model are non-nodule specificfeatures.

In various embodiments, greater than 50% of the top 3 features with thehighest feature importance values are non-nodule specific features. Invarious embodiments, 2 of the top 3 features with the highest featureimportance values are non-nodule specific features. In variousembodiments, 3 of the top 5 features with the highest feature importancevalues are non-nodule specific features. In various embodiments, 4 ofthe top 5 features with the highest feature importance values arenon-nodule specific features. In various embodiments, greater than 50%of the top 5 features with the highest feature importance values arenon-nodule specific features. In various embodiments, greater than 50%of the top 10 features with the highest feature importance values arenon-nodule specific features. In various embodiments, 6, 7, 8, 9, or 10of the top 10 features with the highest feature importance values arenon-nodule specific features.

In various embodiments, a risk prediction model including non-nodulespecific features that more heavily influence the prediction of futurerisk of cancer in comparison to the nodule specific features is a M yearrisk prediction model (e.g., predicts risk of developing cancer within Myears), where M is not equal to 1 year. In various embodiments, M isgreater than or equal to 2 years. For example, M can be 1.5 years, 2years, 2.5 years, 3 years, 3.5 years, 4 years, 4.5 years, 5 years, 5.5years, 6 years, 6.5 years, 7 years, 7.5 years, 8 years, 8.5 years, 9years, 9.5 years, 10 years, 10.5 years, 11 years, 11.5 years, 12 years,12.5 years, 13 years, 13.5 years, 14 years, 14.5 years, 15 years, 15.5years, 16 years, 16.5 years, 17 years, 17.5 years, 18 years, 18.5 years,19 years, 19.5 years, or 20 years. As a specific example, the riskprediction model is a 3 year risk prediction model that predicts risk ofdeveloping cancer within 3 years. As another specific example, the riskprediction model is a 5 year risk prediction model that predicts risk ofdeveloping cancer within 5 years.

In various embodiments, a risk prediction model including non-nodulespecific features that more heavily influence the prediction of futurerisk of cancer in comparison to the nodule specific features is a riskprediction model trained on training images that do not include lungnodules. In various embodiments, such a risk prediction model can betrained on training images derived from individuals classified inLung-RADS 1. In various embodiments, such a risk prediction model can betrained on training images derived from individuals classified inLung-RADS 2. In various embodiments, such a risk prediction model can betrained on training images derived from individuals classified inLung-RADS 3. In various embodiments, such a risk prediction model can betrained on training images derived from individuals classified inLung-RADS 1, Lung-RADS 2, Lung-RADS 3, or any combination thereof. Invarious embodiments, such a risk prediction model is a Lung-RADS 1prediction model, a Lung-RADS 1-2 prediction model, or a Lung-RADS 1-3prediction model.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 1 year, Lung-RADS1-4B prediction model. For example, as shown in Table 3, 6 of the top 10features in terms of feature importance for a 1 year, Lung-RADS 1-4Bprediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 3 year, Lung-RADS1-4B prediction model. For example, as shown in Table 3, 6 of the top 10features in terms of feature importance for a 3 year, Lung-RADS 1-4Bprediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 5 year, Lung-RADS1-4B prediction model. For example, as shown in Table 3, 3 of the top 5features in terms of feature importance for a 5 year, Lung-RADS 1-4Bprediction model are non-nodule specific features. Additionally, 7 ofthe top 10 features in terms of feature importance for a 5 year,Lung-RADS 1-4B prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 5 year, Lung-RADS1-4A prediction model. For example, as shown in Table 6, 3 of the top 5features in terms of feature importance for a 5 year, Lung-RADS 1-4Aprediction model are non-nodule specific features. Additionally, 6 ofthe top 10 features in terms of feature importance for a 5 year,Lung-RADS 1-4A prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 1 year, Lung-RADS1-3 prediction model. For example, as shown in Table 9, 3 of the top 5features in terms of feature importance for a 1 year, Lung-RADS 1-3prediction model are non-nodule specific features. Additionally, 7 ofthe top 10 features in terms of feature importance for a 1 year,Lung-RADS 1-3 prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 3 year, Lung-RADS1-3 prediction model. For example, as shown in Table 9, 2 of the top 3features in terms of feature importance for a 3 year, Lung-RADS 1-3prediction model are non-nodule specific features. Additionally, 4 ofthe top 5 features in terms of feature importance for a 3 year,Lung-RADS 1-3 prediction model are non-nodule specific features.Additionally, 8 of the top 10 features in terms of feature importancefor a 3 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures. As another example, as shown in Table 21, 2 of the top 3features in terms of feature importance for a 3 year, Lung-RADS 1-3prediction model are non-nodule specific features. Additionally, 3 ofthe top 5 features in terms of feature importance for a 3 year,Lung-RADS 1-3 prediction model are non-nodule specific features.Additionally, 8 of the top 10 features in terms of feature importancefor a 3 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 5 year, Lung-RADS1-3 prediction model. For example, as shown in Table 9, 2 of the top 3features in terms of feature importance for a 5 year, Lung-RADS 1-3prediction model are non-nodule specific features. Additionally, 4 ofthe top 5 features in terms of feature importance for a 5 year,Lung-RADS 1-3 prediction model are non-nodule specific features.Additionally, 9 of the top 10 features in terms of feature importancefor a 5 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 1 year, Lung-RADS1-2 prediction model. For example, as shown in Table 12, 4 of the top 5features in terms of feature importance for a 1 year, Lung-RADS 1-2prediction model are non-nodule specific features. Additionally, 9 ofthe top 10 features in terms of feature importance for a 1 year,Lung-RADS 1-2 prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 3 year, Lung-RADS1-2 prediction model. For example, as shown in Table 12, the top 3features in terms of feature importance for a 3 year, Lung-RADS 1-2prediction model are non-nodule specific features. Additionally, the top5 features in terms of feature importance for a 3 year, Lung-RADS 1-2prediction model are non-nodule specific features. Additionally, 8 ofthe top 10 features in terms of feature importance for a 3 year,Lung-RADS 1-2 prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 5 year, Lung-RADS1-2 prediction model. For example, as shown in Table 12, the top 3features in terms of feature importance for a 5 year, Lung-RADS 1-2prediction model are non-nodule specific features. Additionally, the top5 features in terms of feature importance for a 5 year, Lung-RADS 1-2prediction model are non-nodule specific features. Additionally, the top10 features in terms of feature importance for a 5 year, Lung-RADS 1-2prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 1 year, Lung-RADS 1prediction model. For example, as shown in Table 15, the top 3 featuresin terms of feature importance for a 1 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 5 featuresin terms of feature importance for a 1 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 10features in terms of feature importance for a 1 year, Lung-RADS 1prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 3 year, Lung-RADS 1prediction model. For example, as shown in Table 15, the top 3 featuresin terms of feature importance for a 3 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 5 featuresin terms of feature importance for a 3 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 10features in terms of feature importance for a 3 year, Lung-RADS 1prediction model are non-nodule specific features.

In particular embodiments, such a risk prediction model (e.g., a riskprediction model where non-nodule specific features of the riskprediction model have higher feature importance values than the nodulespecific features of the risk prediction model) is a 5 year, Lung-RADS 1prediction model. For example, as shown in Table 15, the top 3 featuresin terms of feature importance for a 5 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 5 featuresin terms of feature importance for a 5 year, Lung-RADS 1 predictionmodel are non-nodule specific features. Additionally, the top 10features in terms of feature importance for a 5 year, Lung-RADS 1prediction model are non-nodule specific features.

In various embodiments, the trained risk prediction model includes a setof trained parameters such that when the risk prediction model isdeployed, the set of trained parameters are used to modify values ofnon-nodule specific features and nodule specific features of an image togenerate a prediction of future risk of cancer for a subject. Thus, theset of trained parameters of the trained risk prediction model are setduring the training phase. For example, the set of trained parametersare set such that the non-nodule specific features more heavilyinfluence the future risk prediction than the nodule specific features.As another example, the set of trained parameters are set such that thenodule specific features more heavily influence the future riskprediction than the non-nodule specific features.

For example, if the risk prediction model is a neural network, one ormore nodes of the neural network that correspond to non-nodule specificfeatures are assigned greater weights (e.g., parameters) than one ormore nodes of the neural network that correspond to nodule specificfeatures. As another example, if the risk prediction model is a randomforest model that weighs non-nodule specific features more heavily thannodule specific features.

In various embodiments, the risk prediction models may also be trainedto predict a location of that future cancer. In various embodiments,training images are divided into different regions and therefore, thetraining of the risk predictions models are performed according to thedifferent regions. Example different regions can include the upper,middle, and lower third of the lungs by volume or separate lobes of thelungs. It will be understood by those of skill in the art that the lungmay be divided into any number of regions having any number ofconfigurations. Enabling risk prediction models to predict locations offuture cancers can guide the selection of interventions, such asregional diagnostic evaluations and therapeutic intervention usinginhaled and bronchoscopically administered drugs and devices.

In various embodiments, risk prediction models disclosed herein achievea performance metric. Example performance metrics include an area underthe curve (AUC) of a receiver operating curve, a positive predictivevalue, and/or a negative predictive value. In various embodiments, riskprediction models disclosed herein exhibit an AUC value of at least 0.5.In various embodiments, risk prediction models disclosed herein exhibitan AUC value of at least 0.6. In various embodiments, risk predictionmodels disclosed herein exhibit an AUC value of at least 0.7. In variousembodiments, risk prediction models disclosed herein exhibit an AUCvalue of at least 0.8. In various embodiments, risk prediction modelsdisclosed herein exhibit an AUC value of at least 0.9. In variousembodiments, risk prediction models disclosed herein exhibit an AUCvalue of at least 0.95. In various embodiments, risk prediction modelsdisclosed herein exhibit an AUC value of at least 0.99. In variousembodiments, risk prediction models disclosed herein exhibit an AUCvalue of at least 0.51, at least 0.52, at least 0.53, at least 0.54, atleast 0.55, at least 0.56, at least 0.57, at least 0.58, at least 0.59,at least 0.60, at least 0.61, at least 0.62, at least 0.63, at least0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, atleast 0.69, at least 0.70, at least 0.71, at least 0.72, at least 0.73,at least 0.74, at least 0.75, at least 0.76, at least 0.77, at least0.78, at least 0.79, at least 0.80, at least 0.81, at least 0.82, atleast 0.83, at least 0.84, at least 0.85, at least 0.86, at least 0.87,at least 0.88, at least 0.89, at least 0.90, at least 0.91, at least0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, atleast 0.97, at least 0.98, or at least 0.99.

VI. Example Method for Predicting Future Risk of Cancer

FIG. 3 is an example flow process for determining a future risk ofcancer for a subject for uses such as patient enrichment, in accordancewith an embodiment. Step 305 involves predicting future lung cancer riskfor a plurality of subjects. Step 305 includes steps 310, 320, 330, and340 which can be repeatedly performed for different subjects in theplurality of subjects.

At step 310, an image is captured from a subject. In variousembodiments, the image is a thoracic CT scan captured from the subject.

At step 320, features are extracted from the image. In variousembodiments, the features include one or both of non-nodule specificfeatures and nodule specific features.

Step 330 is an optional step that involves determining whether thesubject is a candidate subject for future risk prediction based on oneor more of the extracted features. In various embodiments, step 330involves analyzing nodule specific features to determine that thesubject is a candidate subject. For example, step 330 can involveanalyzing nodule specific features to determine that the subject doesnot have a lung nodule or does not have lung cancer and therefore, iseligible to undergo future risk of cancer analysis.

Step 340 involves applying a risk prediction model to analyze theextracted features of the obtained image to predict future cancer risk.In various embodiments, the risk prediction model analyzes bothnon-nodule specific features and nodule specific features. In variousembodiments, the non-nodule specific features have higher featureimportance values than the nodule specific features. Therefore, thenon-nodule specific features more heavily influence the future cancerrisk prediction in comparison to the nodule specific features.

Step 350 involves performing enrichment across the plurality of subjectsusing the predicted future cancer risks. Here, subjects that arepredicted to develop cancer within a period of time, as indicated bytheir predicted future cancer risk, can be included in one or more apatient cohort for enrollment in a clinical trial. Altogether, thisenables the enrollment of reduced numbers of individuals in clinicaltrials.

VII. Cancers

Methods described herein involve implementing risk prediction models forpredicting future risk of cancer. In various embodiments, the cancer inthe subject can include one or more of: lymphoma, B cell lymphoma, Tcell lymphoma, mycosis fungoides, Hodgkin's Disease, myeloid leukemia,bladder cancer, brain cancer, nervous system cancer, head and neckcancer, squamous cell carcinoma of head and neck, kidney cancer, lungcancer, neuroblastoma/glioblastoma, ovarian cancer, pancreatic cancer,prostate cancer, skin cancer, liver cancer, melanoma, squamous cellcarcinomas of the mouth, throat, larynx, and lung, colon cancer,cervical cancer, cervical carcinoma, breast cancer, and epithelialcancer, renal cancer, genitourinary cancer, pulmonary cancer, esophagealcarcinoma, stomach cancer, thyroid cancer, head and neck carcinoma,large bowel cancer, hematopoietic cancer, testicular cancer, colonand/or rectal cancer, uterine cancer, or prostatic cancer. In someembodiments, the cancer in the subject can be a metastatic cancer,including any one of bladder cancer, breast cancer, colon cancer, kidneycancer, lung cancer, melanoma, ovarian cancer, pancreatic cancer,prostatic cancer, rectal cancer, stomach cancer, thyroid cancer, oruterine cancer. In particular embodiments, the cancer is a lung cancer.In particular embodiments, the cancer is a type of lung cancer,including any one of small cell lung cancer, non-small cell lung cancer,non-small cell carcinoma, adenocarcinoma, squamous cell cancer, largecell carcinoma, small cell carcinoma, combined small cell carcinoma,neuroendocrine tumor, lung sarcoma, lung lymphoma, bronchial carcinoids.

In various embodiments, risk prediction models described herein predicta future risk of a presence of cancer, such as a lung cancer. In otherwords, risk prediction models predict for a risk of a presence orabsence of cancer, such as a lung cancer. In various embodiments, riskprediction models described herein predict a future risk of a subtype oflung cancer, including any one of small cell lung cancer, non-small celllung cancer, non-small cell carcinoma, adenocarcinoma, squamous cellcancer, large cell carcinoma, small cell carcinoma, combined small cellcarcinoma, neuroendocrine tumor, lung sarcoma, lung lymphoma, bronchialcarcinoids. In other words, risk prediction models classify a subject aslikely to develop a particular subtype of lung cancer within a timeperiod (e.g., 1, 3, or 5 years). In particular embodiments, riskprediction models predict a future risk of non-small cell lung cancer orsmall cell lung cancer.

VIII. Interventions

Embodiments described herein involve the implementing risk predictionmodels for predicting future risk of cancer. In various embodiments, anintervention is provided to a subject based on the future risk of cancerprediction. In various embodiments, the intervention can be any one of:application of a diagnostic, application of a prophylactic therapeuticagent, or a subsequent action. Example subsequent actions can include asubsequent testing of the subject to confirm whether the subjectdevelops cancer. Subsequent testing can include any of a subsequentbiopsy (e.g., cancer biopsy or lymph node biopsy) or subsequent imagescanning (e.g., CT scanning, PET scanning, MRI scanning, ultrasoundimaging, or X-ray imaging) In various embodiments, subsequent testing ofthe subject can during at a next scheduled visit or at a pre-determinedamount of time (e.g., 1 month, 2 months, 3 months, 4 months, 5 months, 6months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months,18 months, or 24 months) after predicting the future risk of cancer. Invarious embodiments, additional subsequent actions can includesubsequent actions to treat a cancer that has developed in the subject,such as tumor resection, bronchoscopic diagnosis, selection and/oradministration of therapeutic(s), selection/administration ofpharmaceutical composition, or any combination thereof.

In various embodiments, a therapeutic agent can be selected and/oradministered to the subject based on the predicted future risk ofcancer. The selected therapeutic agent is likely to delay or prevent thedevelopment of the cancer, such as lung cancer. Exemplary therapeuticagents include chemotherapies, energy therapies (e.g., external beam,microwave, radiofrequency ablation, brachytherapy, electroporation,cryoablation, photothermal ablation, laser therapy, photodynamictherapy, electrocauterization, chemoemboilization, high intensityfocused ultrasound, low intensity focused ultrasound), antigen-specificmonoclonal antibodies, anti-inflammatories, oncolytic viral therapies,or immunotherapies. In various embodiments, the selected therapeuticagent is an energy therapy and the amount (e.g., dose and duration) ofthe energy applied can be tailored to achieve a desired therapeuticeffect. In various embodiments the therapeutic agent is a small moleculeor biologic, e.g. a cytokine, antibody, soluble cytokine receptor,anti-sense oligonucleotide, siRNA, etc. Such biologic agents encompassmuteins and derivatives of the biological agent, which derivatives caninclude, for example, fusion proteins, PEGylated derivatives,cholesterol conjugated derivatives, and the like as known in the art.Also included are antagonists of cytokines and cytokine receptors, e.g.traps and monoclonal antagonists. Also included are biosimilar orbioequivalent drugs to the active agents set forth herein.

Therapeutic agents for lung cancer can include chemotherapeutics such asdocetaxel, cisplatin, carboplatin, gemcitabine, Nab-paclitaxel,paclitaxel, pemetrexed, gefitinib, erlotinib, brigatinib (Alunbrig®),capmatinib (Tabrecta®), selpercatinib (Retevmo®), entrectinib(Rozlytrek®), lorlatinib (Lorbrena®), larotrectinib (Vitrakvi®),dacomitinib (Vizimpro®), and vinorelbine. Therapeutic agents for lungcancer can include antibody therapies such as durvalumab (Imfinzi®),nivolumab (Opdivo®), pembrolizumab (Keytruda®), atezolizumab(Tecentriq®), canakinumab, and ramucirumab.

In various embodiments, one or more of the therapeutic agents describedcan be combined as a combination therapy for treating the subject.

In various embodiments, a pharmaceutical composition can be selectedand/or administered to the subject based on the subject level risk ofmetastatic cancer, the selected therapeutic agent likely to exhibitefficacy against the cancer. A pharmaceutical composition administeredto an individual includes an active agent such as the therapeutic agentdescribed above. The active ingredient is present in a therapeuticallyeffective amount, i.e., an amount sufficient when administered to treata disease or medical condition mediated thereby. The compositions canalso include various other agents to enhance delivery and efficacy, e.g.to enhance delivery and stability of the active ingredients. Thus, forexample, the compositions can also include, depending on the formulationdesired, pharmaceutically acceptable, non-toxic carriers or diluents,which are defined as vehicles commonly used to formulate pharmaceuticalcompositions for animal or human administration. The diluent is selectedso as not to affect the biological activity of the combination. Examplesof such diluents are distilled water, buffered water, physiologicalsaline, PBS, Ringer's solution, dextrose solution, and Hank's solution.In addition, the pharmaceutical composition or formulation can includeother carriers, adjuvants, or non-toxic, nontherapeutic, nonimmunogenicstabilizers, excipients and the like. The compositions can also includeadditional substances to approximate physiological conditions, such aspH adjusting and buffering agents, toxicity adjusting agents, wettingagents and detergents. The composition can also include any of a varietyof stabilizing agents, such as an antioxidant.

The pharmaceutical compositions or therapeutic agents described hereincan be administered in a variety of different ways. Examples includeadministering a composition containing a pharmaceutically acceptablecarrier via oral, intranasal, intramodular, intralesional, rectal,topical, intraperitoneal, intravenous, intramuscular, subcutaneous,subdermal, transdermal, intrathecal, endobronchial, transthoracic, orintracranial method.

In various embodiments, a clinical response can be provided to thesubject based on the predicted future risk of cancer generated for thesubject by implementing risk prediction models. In various embodiments,a clinical response can include providing counseling to modify abehavior of the subject (e.g., counsel the patient about smokingcessation to reduce risk), initiating of an inhaled/topical, intravenousor enteral (by mouth) therapeutic that could delay/prevent malignanttransformation, slow tumor growth or even prevent spread of disease(metastasis), establishing an adaptive screening schedule for futurerisk similar to what is done with colonoscopy for polyps (e.g.,individuals predicted to be higher risk for future lung cancer shouldhave more frequent follow up and imaging), or performing or schedulingto be performed an additional risk prediction test to confirm thepredicted future risk of lung cancer (e.g., persons deemed to be higherrisk for lung cancer may also then undergo additional testing to eitherconfirm that risk or narrow the cancer type the person is at greatestrisk for. In various embodiments, the additional risk prediction testcould include blood based biomarkers (to look for non-specificinflammation which is a known risk for lung cancer),metabolomics/proteomics/gene expression/genetic sequencing. The personcould also have additional sampling of tissue (nasal epithelium,bronchial epithelium, etc) to look at changes in gene expression in therespiratory tract.)

IX. Computer Implementation

The methods of the invention, including the methods of implementing riskprediction models for predicting future risk of cancer, are, in someembodiments, performed on one or more computers.

For example, the building and deployment of a risk prediction model canbe implemented in hardware or software, or a combination of both. In oneembodiment of the invention, a machine-readable storage medium isprovided, the medium comprising a data storage material encoded withmachine readable data which, when using a machine programmed withinstructions for using said data, is capable of executing the trainingor deployment of risk prediction models and/or displaying any of thedatasets or results (e.g., future risk of cancer predictions forsubjects) described herein. The invention can be implemented in computerprograms executing on programmable computers, comprising a processor, adata storage system (including volatile and non-volatile memory and/orstorage elements), a graphics adapter, a pointing device, a networkadapter, at least one input device, and at least one output device. Adisplay is coupled to the graphics adapter. Program code is applied toinput data to perform the functions described above and generate outputinformation. The output information is applied to one or more outputdevices, in known fashion. The computer can be, for example, a personalcomputer, microcomputer, or workstation of conventional design.

Each program can be implemented in a high-level procedural orobject-oriented programming language to communicate with a computersystem. However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language can be a compiled orinterpreted language. Each such computer program is preferably stored ona storage media or device (e.g., ROM or magnetic diskette) readable by ageneral or special purpose programmable computer, for configuring andoperating the computer when the storage media or device is read by thecomputer to perform the procedures described herein. The system can alsobe considered to be implemented as a computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

The signature patterns and databases thereof can be provided in avariety of media to facilitate their use. “Media” refers to amanufacture that contains the signature pattern information of thepresent invention. The databases of the present invention can berecorded on computer readable media, e.g. any medium that can be readand accessed directly by a computer. Such media include, but are notlimited to: magnetic storage media, such as floppy discs, hard discstorage medium, and magnetic tape; optical storage media such as CD-ROM;electrical storage media such as RAM and ROM; and hybrids of thesecategories such as magnetic/optical storage media. One of skill in theart can readily appreciate how any of the presently known computerreadable mediums can be used to create a manufacture comprising arecording of the present database information. “Recorded” refers to aprocess for storing information on computer readable medium, using anysuch methods as known in the art. Any convenient data storage structurecan be chosen, based on the means used to access the stored information.A variety of data processor programs and formats can be used forstorage, e.g. word processing text file, database format, etc.

In some embodiments, the methods of the invention, including the methodsfor predicting a future risk of cancer by implementing risk predictionmodels, are performed on one or more computers in a distributedcomputing system environment (e.g., in a cloud computing environment).In this description, “cloud computing” is defined as a model forenabling on-demand network access to a shared set of configurablecomputing resources. Cloud computing can be employed to offer on-demandaccess to the shared set of configurable computing resources. The sharedset of configurable computing resources can be rapidly provisioned viavirtualization and released with low management effort or serviceprovider interaction, and then scaled accordingly. A cloud-computingmodel can be composed of various characteristics such as, for example,on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, and so forth. A cloud-computing model canalso expose various service models, such as, for example, Software as aService (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure asa Service (“IaaS”). A cloud-computing model can also be deployed usingdifferent deployment models such as private cloud, community cloud,public cloud, hybrid cloud, and so forth. In this description and in theclaims, a “cloud-computing environment” is an environment in which cloudcomputing is employed.

FIG. 4 illustrates an example computer for implementing the entitiesshown in FIGS. 1A, 1B, 2A, 2B, and 3 . The computer 400 includes atleast one processor 402 coupled to a chipset 404. The chipset 404includes a memory controller hub 420 and an input/output (I/O)controller hub 422. A memory 406 and a graphics adapter 412 are coupledto the memory controller hub 420, and a display 418 is coupled to thegraphics adapter 412. A storage device 408, an input device 414, andnetwork adapter 416 are coupled to the I/O controller hub 422. Otherembodiments of the computer 400 have different architectures.

The storage device 408 is a non-transitory computer-readable storagemedium such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. The memory 406 holds instructionsand data used by the processor 402. The input interface 414 is atouch-screen interface, a mouse, track ball, or other type of pointingdevice, a keyboard, or some combination thereof, and is used to inputdata into the computer 400. In some embodiments, the computer 400 may beconfigured to receive input (e.g., commands) from the input interface414 via gestures from the user. The network adapter 416 couples thecomputer 400 to one or more computer networks.

The graphics adapter 412 displays images and other information on thedisplay 418. In various embodiments, the display 418 is configured suchthat the user may (e.g., radiologist, oncologist, pulmonologist) mayinput user selections on the display 418 to, for example, initiate riskprediction for a patient, order any additional exams or proceduresand/or set parameters for the risk prediction models. In one embodiment,the display 418 may include a touch interface. In various embodiments,the display 418 can show one or more future risk of cancer predictionsfor a subject. Thus, a user who accesses the display 418 can inform thesubject of the future risk of cancer that is predicted for the subject.In various embodiments, the display 418 can show information such as thefeatures that most heavily contributed to the future risk of cancerprediction for a subject. For example, a subject predicted to have afuture risk of cancer can be largely due to a percentage of thesubject's lung occupied by centrilobular emphysema. Thus, theidentification of the feature and/or the value of the feature (e.g.,percentage of the subject's lung occupied by centrilobular emphysema)can be shown on the display 418 to a user e.g., clinician user. Invarious embodiments, the top 1, top 2, top 3, top 4, top 5, top 6, top7, top 8, top 9, or top 10 features that most heavily contributed to thefuture risk of cancer prediction for the subject can be shown on thedisplay 418. Displaying the top contributing features can providecontext to a user e.g., clinician user in understanding the featuresthat resulted in the future risk of cancer prediction. Patient profiles,CT images, generated risk assessments and any other relevant informationmay be stored to the memory so that patient information/results may beaccessible at any given time.

The computer 400 is adapted to execute computer program modules forproviding functionality described herein. As used herein, the term“module” refers to computer program logic used to provide the specifiedfunctionality. Thus, a module can be implemented in hardware, firmware,and/or software. In one embodiment, program modules are stored on thestorage device 408, loaded into the memory 406, and executed by theprocessor 402.

The types of computers 400 used by the entities of FIG. 1A or 1B canvary depending upon the embodiment and the processing power required bythe entity. For example, the cancer prediction system 130 can run in asingle computer 400 or multiple computers 400 communicating with eachother through a network such as in a server farm. The computers 400 canlack some of the components described above, such as graphics adapters412, and displays 418.

X. Systems

Further disclosed herein are systems for implementing risk predictionmodels for predicting future risk of cancer. In various embodiments,such a system can include at least the cancer prediction system 130described above in FIG. 1A. In various embodiments, the cancerprediction system 130 is embodied as a computer system, such as acomputer system with example computer 400 described in FIG. 4 .

In various embodiments, the system includes an imaging device, such asimaging device 120 described above in FIG. 1A. In various embodiments,the system includes both the cancer prediction system 130 (e.g., acomputer system) and an imaging device. In such embodiments, the cancerprediction system 130 can be communicatively coupled with the imagingdevice 120 to receive images (e.g., CT scans) captured from a subject.The computer system implements, in silico, risk prediction models toanalyze the images and to determine a future risk of lung cancer for thesubject.

XI. Additional Embodiments

There has been extensive investigation to determine the earliestmolecular changes that mark the transition from normal tissue repair touncontrolled cell growth and cancer. Much of this work has focused onidentifying who is most resilient or susceptible to lung injury fromsmoking. These patterns of injury include distal small airway diseaseleading to expiratory airflow obstruction, emphysematous destruction ofthe lung parenchyma, aberrant deposition of collagen and scar tissue(interstitial change and fibrosis) and pulmonary vascular remodeling. Itis increasingly clear that the development of some admixture of thesepatterns of lung injury, all unique manifestations of susceptibility,increases the risk of developing lung cancer.

Observational studies have repeatedly demonstrated that smokers whodevelop expiratory airflow obstruction (a defining feature ofCOPD—Chronic Obstructive Pulmonary Disease) are at increased risk forfuture lung cancer. That risk is further increased in those people withCOPD who have emphysematous destruction of their distal airspaces.Additional investigation has linked interstitial lung disease andpulmonary fibrosis (another form of smoking related lung injury) to aheightened risk of lung cancer, again supporting the general contentionthat conditions characterized by chronic injury and repair may lead tomalignancy. Lastly, there are extra-pulmonary manifestations ofsusceptibility to chronic tobacco smoke exposure such as muscle wastingthat may also identify those at increased risk of disease, possiblybecause of a direct link between the musculature and cancer but alsobecause the loss of fat free mass and skeletal musculature may be areflection of inflammatory overspill from already injured lungs. All ofthese pulmonary and extrapulmonary manifestations of injury from chronictobacco smoke exposure are detectable on computed tomographic (CT)imaging.

The present disclosure may be understood with reference to the followingdescription and the appended drawings. The present disclosure relates toa system and method integrating expertise in medical image analysis andapplied advanced machine learning techniques to metrics extracted frommedical images obtained in the National Lung Screening Trial (NLST).Features extracted from these medical images are used to generate a riskmodel to predict future lung cancer. In particular, the risk modelidentifies a pattern/admixture of features that could be used toidentify smokers with an increased risk of future lung cancer.

The National Lung Screening Trial (NLST) was a randomized controlledtrial to determine if annual CT scanning could reduce death due to lungcancer. The primary hypothesis that drove this investigation was that CTimaging provides higher resolution in-vivo data that would detect cancerat an earlier stage (or smaller size nodule) which is more amenable totreatment and cure. The investigation recruited approximately 53,000smokers, randomizing half to annual CT scanning and the other half toannual chest X-ray (CXR). Those smokers who underwent annual CT scanningexperienced an approximate 20% reduction in mortality due to lungcancer. These exciting results led to a change in health care with lungcancer screening CTs being a reimbursed part of preventive medicine.

The NLST CT scans and clinical data are now freely available to thebiomedical community and the baseline (T0), year 1 (T1) and year 2 (T2)CT scans from 15,000 individuals were obtained. According to anexemplary embodiment, image analytic algorithms were applied to all ofthese scans to extract measures of emphysema, interstitial change,preserved lung tissue and pectoralis muscle size (area) where the lattermuscle measure is used as a proxy for body composition or fat free mass.In one embodiment, the 15,000 T0 CT scans were divided in half to builda dedicated training set of data (n=7,500) and testing set of data(n=7,500). Using the objective features extracted from the CT images aswell as the subjective reports of visually ascertained nodules and theircharacteristics (size, shape, etc.), a robust model to predict futurelung cancer was created. The model strategy was based on a Random Forestapproach to develop and optimize decision trees in the training data topredict a desired outcome. This approach was used rather than selectinga fixed threshold for a certain burden of disease (i.e. >10% emphysemaor >10% interstitial change) because there are almost certainly severalcombinations of emphysema, interstitial change and sarcopenia thatidentify a heightened risk of lung cancer. For example, a smoker having25% of their lung with emphysema may have the same heightened risk forfuture lung cancer as the smoker with 2% emphysema, 12% interstitialchange and decreased pectoralis muscle area.

The random forest-based models were independently trained to predict 1,3 and 5-year risk of future lung cancer in smokers enrolled in the NLST.These models were then modified to enable prediction of incident (new)cancer rather than just identify those with prevalent (already present)cancer on the T0 CT scan. This final step leveraged visual datadescribing features of any lung nodules in the CT image. Although theexemplary embodiments show and describe random forest-based models, itwill be understood by those of skill in the art that other modelingapproaches such as, for example, logistic regression and XGBoost mayalso be utilized. It will also be understood by those of skill in theart that although the risk prediction models are specifically shown anddescribed as providing 1, 3 and 5 year risk predictions, predictionterms may be varied, as desired.

The life course of lung cancer most commonly begins with the developmentof a lung nodule. This nodule may be cancerous or may be a benignovergrowth of tissue that subsequently undergoes malignanttransformation. The Radiologic community recognizes this process, and inan attempt to standardize medical management developed a scoring systemto stage nodules discovered on CT scan by their likelihood of beingcancer. This staging system is called Lung CT screening, Reporting andData System (LungRADS) and is based upon the size of the nodule, therate of growth of the nodule and the appearance of the nodule.Generally, the larger the nodule, the more rapid its growth or the moreirregular it is in appearance, the more likely it is to be cancer.

In one embodiment, the LungRADS scoring system was used to remove peoplefrom the risk model that were most likely to have prevalent cancer atthe time of the T0 scan. To do this, people with the highest LungRADSscore (4A, 4B and 4X) were excluded from this specific analysis (butwere included in other models) while keeping people with smaller, lowerrisk nodules (2 and 3) that may become cancer at a future date. Themodels for 1, 3 and 5-year risk prediction were retrained afterexcluding all people with LungRADS 4 nodules and then applied to thetesting sub cohort (n=7,500) of the NLST.

A combination of radiologic features could still identify people with a10+% absolute 3-year risk of developing lung cancer. Further,approximately ⅓ of those people did not have a nodule on the baseline T0scan suggesting that these models are not just predicting which nodulesmay become cancerous but rather which people may develop a nodule andthen be diagnosed with lung cancer. A series of risk prediction modelswere generated using this approach. These models varied by the time tocancer (1, 3 and 5-year risk) as well as the nature of the nodules thatwere excluded. This submission therefore includes all models across theLungRADS stages, including LungRADS 4A, 4B and 4X.

Additional radiologic features such as vascular and airwaycalcification, pulmonary vascular morphology and bone mineral densitymay also represent unique metrics of disease susceptibility that can beused to predict future lung cancer. Nodule specific features may also beintegrated into these models. Nodule specific features may include, forexample, nodule location, proximity to emphysema and interstitialchange, rate of change of nodule characteristics (both absolute andrelative to normative standards).

In a further embodiment, these image-based models may be integrated intomore comprehensive biomarker panels for risk assessment and nodulediscrimination. Those panels include but are not limited to proteomics,genetics, gene expression, cell free circulating tumor DNA, etc., whereit is expected that optimal model performance may result forcombinations and highly sensitive and specific markers.

These risk prediction models are predicated on a mixture of objectiveand subjective features extracted from the CT image. Extensive priorinvestigation suggests that objective and not subjective features on CTvary by CT scanner brand, generation and image reconstruction software.Additional geographic variability in the burden of disease betweensubjects may reflect actual differences to noxious exposure (i.e. somepeople may be more or less susceptible to injury from exposure and moreor less likely to develop emphysema or interstitial changes in the lungtissue). For these reasons, absolute thresholds to determine thepresence and severity of CT metrics of disease were not used. Instead,the data in the test sub cohort were each normalized by subtracting themean and dividing by the standard deviation for each covariable. Asadditional cohorts are aggregated for model refinement, datanormalization will be performed using all existing data (cross cohort)or select subsets of the data including but not limited to those thoughtto best reflect the patient specific biology, exposure history,ethnicity or type of medical image being processed. Once normalized, thedata were used in the random forest-based approach to modeling.

In one embodiment, objective features include, for example, lungparenchyma features (e.g., densitometric measures of the lung parenchymaand measures of interstitial changes in the lung parenchyma) and bodycomposition measures of the musculature/chest wall. Densitometricmeasures of the lung parenchyma may include, for example, the percentageof the lung occupied by (i) low attenuation area (LAA), which is definedas the area/volume having an attenuation less than −950 Hounsfield Units(HU) and (ii) high attenuation area (HAA), which is defined as thearea/volume of lung having attenuation between −600 HU and −250 HU, andthe ratio between LAA in the upper lung zone to that in the lower lungzone. Measures of interstitial changes in the lung parenchyma includelocal histogram measures of the lung parenchyma, the percentage of lungoccupied by, for example, normal tissue, centrilobular emphysema,centrilobular nodule, ground glass, honeycombing, linear scar, nodular,reticular, subpleural line, other emphysema and/or cyst. Bodycomposition measures of the musculature/chest wall may include, forexample, pectoralis major cross-sectional area, pectoralis minorcross-sectional area, pectoralis major lean cross-sectional area,pectoralis minor lean cross-sectional area, aggregate cross-sectionalarea of the left or right pectoralis major or minor muscles, andsubcutaneous fat cross-sectional area.

The risk prediction models trained using the data from the NLST providea multi-modal risk assessment and enrichment approach. For example,consider two patients who developed cancer within 3 years of enrollmentin NLST. Both had Lung-RADS 3 nodules at baseline, both were predictedto develop cancer within 3 years by the algorithm and both developedstage IA adenocarcinoma within 3 years. In fact, the predictedprobabilities of developing cancer for both patients are within 2% ofeach other (84.8% for Patient 1 and 86.4% for Patient 2). Patient 1 wasa 65-year-old white female former smoker with a 76 pack year smokinghistory, a BMI of 23.5, relatively small pectoralis mass, andsignificant, upper lobe predominant emphysema (31% emphysema by volumebased on local histogram analysis). A 76 pack year smoking history isequivalent to smoking a pack (20) of cigarettes everyday for 76 years ortwo packs a day for 38 years. By contrast, Patient 2 was a 59 year oldwhite male former smoker with a 43 pack year smoking history, a BMI of29.8, relatively preserved pectoralis mass, and minimal emphysema (2.6%emphysema by volume based on local histogram analysis), butsignificantly more interstitial features (7.1% compared to 4%). In thissecond case it is this last feature and the strong relationship betweeninterstitial features and cancer that likely leads to the algorithmpredicting the development of cancer within 3 years. The clinical andradiologic differences between these two cases highlight the ability ofthe risk prediction models to identify patients with distinct anddiffering phenotypes who are likely to develop cancer.

According to a further embodiment, the models used to predict per personrisk of future cancer may also be used to predict the location of thatfuture cancer. In one embodiment, the data is divided into thirds torepresent the upper, middle, and lower third of the lungs by volume aswell as by lobe. Model training and testing may be performed and theregional (volume by ⅓rds as well as by lobe) risk of future lung cancermay be calculated. These additional data enable focused regionaldiagnostic evaluations and therapeutic intervention using inhaled andbronchoscopically administered drugs and devices. It will be understoodby those of skill in the art that the lung may be divided into anynumber of regions having any number of configurations.

A system according to an exemplary embodiment of the present disclosurepredicts a risk of future lung cancer based on a CT image of the lungsof a patient. The system can comprise a processor along with a userinterface, a display, and a memory. The processor includes riskprediction models, as described above, which may provide apatient-specific assessment regarding a likelihood for developing cancerin future. The risk prediction models may implemented by the processoras, for example, lines of code that are executed by the processor, asfirmware executed by the processor, as a function of the processor beingan application specific integrated circuit (ASIC), etc. It will also beunderstood by those of skill in the art that although the system isshown and described as comprising a computing system comprising a singleprocessor, user interface, display and memory, the system may becomprised of a network of computing systems, each of which includes oneor more of the components described above. In one example, the riskprediction models may be executed via a central processor of a network,which is accessible via a number of different user stations.Alternatively, the risk prediction models may be executed via one ormore processors.

The risk prediction models created to identify people at greatest riskfor future cancer will enable clinical trials focused on cancerprevention and treatment of high risk individuals, as well as to enableclinical care by identifying which people should have the mostaggressive follow up and screening to detect cancer at its earlieststages. The patient-specific risk assessment may be output and displayedto the user on the display of one or more computing stations. Thepatient-specific risk assessment may be provided in any of a variety ofconfigurations. The risk assessment may include a predicted level ofrisk and/or a timeframe for the risk (e.g., 1, 3 and 5-year risk). Inone embodiment, to enable visualization of the modeling data, the CTscans will be color coded by person and by region. Those regions withthe highest risk will be colored red. Moderate risk regions will beyellow and low risk regions will be green. The result of this is that aperson of low risk for future lung cancer may have their whole lungimage shaded green while someone at high risk for future lung cancer mayhave a CT image with regions of red (corresponding to high risk), yellow(moderate risk) and green (low risk). For example, these color-codedmaps may be presented on a display in the Radiology reading room for thechest radiologist (to assist in clinical interpretation) as well as on adisplay of the image based workstation used by the pulmonologist,oncologist and proceduralist to target therapy. According to oneembodiment, these maps will be generated using three colors as well ascontinuous gradients of colors from red to green based upon the use caseand mode of visualization. It will be understood by those of skill inthe art, however, that the maps may be generated using any of a varietyof colors and configurations so long as the generated map indicates apredicted risk of future lung cancer to the user.

The user may (e.g., radiologist, oncologist, pulmonologist) may inputuser selections on the user interface to, for example, initiate riskprediction for a patient, order any additional exams or proceduresand/or set parameters for the risk prediction models. The user interfacemay include input devices such as, for example, a keyboard and/or mouse.In one embodiment, the user interface may include a touch interfaceenabled on the display. Patient profiles, CT images, generated riskassessments and any other relevant may be stored to the memory so thatpatient information/results may be accessible via a user at any giventime.

Image based risk prediction may include predictions for 1, 3 and 5-yearrisk of lung cancer. This series of models will be used to developpreventive therapies for lung cancer by enabling clinical trials andultimately clinical care. Clinical trials will be more efficient andfeasible because the event rate (future lung cancer) will be abovebackground thereby allowing smaller cohort size and potentially reducingthe numbers of patients that need to be treated to reduce one case oflung cancer. Following identification of efficacious therapies for theprevention of lung cancer, this model may be used outside of clinicaltrials as a biomarker in clinical care to identify those people who maybest benefit from treatment. Since all treatments involve some degree ofrisk, knowing who is most likely to develop lung cancer will influencethe risk/benefit decision analysis and reimbursement around theimplementation of therapy in the individual.

According to a further embodiment, the risk prediction models mayprovide a foundation for identifying different risk for cellular typesof lung cancer including adenocarcinoma and squamous cell carcinoma.These differences are based on the cell type from which cancer arose andmay likely influence decisions related to methods of treatment andpatient outcomes.

The risk prediction models described herein provide per patient levelprobabilities of developing lung cancer. These models may be modifiedusing regional metrics of lung susceptibility to predict the futurelocation of the lung cancer in that individual. As bronchoscopicprocedures may increasingly be utilized to diagnose, treat and preventlung cancer, knowledge of the lobe and region of the lobe where lungcancer will develop will facilitate a diagnostic biopsy of a suspiciouslesion or nodule and/or the local administration of therapy to preventmalignant transformation.

The risk prediction models described herein may also be used for theselection of therapies and medical decision making There are competingrisks of disease development and death in all people. For example,smokers may develop lung cancer, heart disease or cerebrovasculardisease, all of which may be aggressive and life threatening. The riskprediction models detailed in this document may be integrated with otherexisting clinically utilized risk models such as the Framingham HeartStudy Risk Score (to predict future heart disease) to provide a moreholistic assessment of how to maintain patient health and inform themabout their most pressing health care needs.

A processor of the system processes a CT image of a patient to extractfeatures related to parenchymal damage and body composition. Note thatadditional modeling may expand the list of those features to includeother data (including but not limited to vascular and airwaycalcification, pulmonary vascular morphology, bone mineral density aswell as nodule specific features such as rate of growth and proximity toboth emphysema and interstitial change). Data is normalized at the persubject level using distributions of the CT features collected in thetraining set. This training set will include multiple cohortsrepresenting differing geographic regions, exposures and tumor biology.Utilization of Random Forest generated risk prediction model to predictrisk of future lung cancer (per person and per lung region). The riskassessment, which in one embodiment may include a color coding of lungbased upon regional risk of future cancer (red, yellow and green torepresent high, medium and low risk of future cancer), may be displayedto the user on the display.

Disclosed herein are additional embodiments including a method,comprising: extracting features from a CT image of a lung of a patient;normalizing data including extracted features from the CT image of thelung of the patient; and generating, using a risk prediction model, arisk assessment including a prediction of a future risk of lung cancer.In various embodiments, the risk assessment includes a color-coded imageof a lung of the patient, based upon regional risk of future cancer. Invarious embodiments, the risk prediction model identifies one of apattern and admixture of lung features indicative of an increased riskin lung cancer. In various embodiments, the extracted features includeone of measures of emphysema, interstitial change, preserved lung issueand pectoralis muscle size.

Additionally disclosed herein is a system, comprising: a non-transitorycomputer readable storage medium storing an executable program; and aprocessor executing the executable program to cause the processor to:extract features from a CT image of a lung of a patient; normalize dataincluding extracted features from the CT image of the lung of thepatient; and generate, using a risk prediction model, a risk assessmentincluding a prediction of a future risk of lung cancer.

EXAMPLES

Below are examples of specific embodiments for carrying out the presentinvention. The examples are offered for illustrative purposes only andare not intended to limit the scope of the present invention in any way.Efforts have been made to ensure accuracy with respect to numbers used(e.g., amounts, temperatures, etc.), but some experimental error anddeviation should be allowed for.

Example 1: Building Risk Prediction Models and Summary of Analysis

Computed tomography (CT) images from the National Lung Cancer ScreeningTrial (NLST) cohort were analyzed to predict likelihood of cancer inpatients across different future horizon timepoints (e.g., within 1year, within 3 years, or within 5 years). The predicted future risk ofcancer was used to identify a population of patients enriched for thedevelopment of lung cancer.

The National Lung Screening Trial (NLST) was a randomized controlledtrial to determine if annual CT scanning could reduce death due to lungcancer. The primary hypothesis that drove this investigation was that CTimaging provides higher resolution in-vivo data that would detect cancerat an earlier stage (or smaller size nodule) which is more amenable totreatment and cure. The investigation recruited approximately 53,000smokers, randomizing half to annual CT scanning and the other half toannual chest X-ray (CXR). Those smokers who underwent annual CT scanningexperienced an approximate 20% reduction in mortality due to lungcancer. These exciting results led to a change in health care with lungcancer screening CTs being a reimbursed part of preventive medicine.

The NLST CT images and clinical data are now freely available to thebiomedical community and CT scans were obtained. The overalldemographics and details of the full patient are shown in Table 2 Imageanalytic algorithms were applied to CT images to extract features suchas emphysema, interstitial change, preserved lung tissue and pectoralismuscle size (area) where the latter muscle measure is used as a proxyfor body composition or fat free mass. The CT scans were divided in halfto build a dedicated training set of data and testing set of data. Usingthe objective features (e.g., non-nodule specific features) extractedfrom the CT images as well as the subjective features of visuallyascertained nodules and their characteristics (e.g., nodule specificfeatures such as nodule size, shape, etc.), a robust model to predictfuture lung cancer was created.

Features (e.g., variables) were extracted from the CT images. Thefeatures included were chosen based on prior experience and based onlikely biologic relevance.

For the risk prediction models described in Examples 2-11 below, thefollowing non-nodule specific features were used:

-   -   1) Densitometric measures of the lung parenchyma        -   a. The percentage of lung occupied by:            -   i. Low attenuation area (LAA), which was defined as the                area/volume having an attenuation less than −950                Hounsfield units (HU)            -   ii. High attenuation area (HAA), which was defined as                the area/volume of lung having attenuation between −600                HU and −250 HU.        -   b. The ratio between LAA in the upper lung zone to that in            the lower lung zone (Ratio LAA)    -   2) Body composition measures of the musculature/chest wall        -   a. Pectoralis major cross-sectional area        -   b. Pectoralis minor cross-sectional area        -   c. Pectoralis major lean cross-sectional area        -   d. Pectoralis minor lean cross-sectional area        -   e. Subcutaneous fat cross-sectional area (axial or coronal)    -   3) Local histogram measures of the lung parenchyma        -   a. The percentage of lung occupied by:            -   i. Normal tissue            -   ii. Centrilobular emphysema            -   iii. Centrilobular nodule            -   iv. Ground glass            -   v. Honeycombing            -   vi. Linear scar            -   vii. Nodular            -   viii. Reticular            -   ix. Subpleural line            -   x. Other emphysema            -   xi. Cyst

For the risk prediction models described in Examples 2-6 below, thefollowing nodule-specific features include:

-   -   1) Attenuation    -   2) Margin description    -   3) Diameter    -   4) Lung CT Screening, Reporting and Data System (Lung-RADS)        Score

For the risk prediction models described in Examples 7-11 below, thefollowing radiomic features (e.g., nodule-specific features) wereextracted from the original CT image, a wavelet transformed CT image,and a Gaussian transformed CT image.

-   -   1) First order statistics    -   2) 3D shape based features    -   3) 2D shape based features    -   4) Gray level cooccurrence matrix    -   5) Gray level run length matrix    -   6) Gray level size zone matrix    -   7) Neighboring gray tone difference matrix    -   8) Gray level dependence matrix.

The features used specifically did not include clinical characteristics,meaning that the clinical characteristics of the overall cohort are theNLST characteristics. The clinical characteristics by lung cancerprediction category are shown below.

Risk prediction models are predicated on a mixture of objective (e.g.,non-nodule specific) and subjective (e.g., nodule specific) featuresextracted from the CT image. Objective and not subjective features on CTvary by CT scanner brand, generation and image reconstruction software.Additional geographic variability in the burden of disease betweensubjects may reflect actual differences to noxious exposure (i.e. somepeople may be more or less susceptible to injury from exposure and moreor less likely to develop emphysema or interstitial changes in the lungtissue). For these reasons, absolute thresholds to determine thepresence and severity of CT metrics of disease were not used. Instead,the data in the test sub cohort were each normalized by subtracting themean and dividing by the standard deviation for each covariable. Asadditional cohorts are aggregated for model refinement, datanormalization will be performed using all existing data (cross cohort)or select subsets of the data including but not limited to those thoughtto best reflect the patient specific biology, exposure history,ethnicity or type of medical image being processed. Once normalized, thedata were used in the random forest-based approach to modeling.

Pre-processing of the data was first conducted. This includednormalizing values of continuous features (e.g., by centering(subtraction of the mean) and scaling (division by the standarddeviation). The normalization of the test set was performed usinginformation from the training set only. That is, the training set meanand standard deviation were used to normalize both the training set andthe test set. Additionally, pre-processing included down sampling of themajority class (no cancer) was performed in order to account forimbalanced data, i.e. to account for the fact that cancer diagnosis isrelatively uncommon. Alternative approaches such as SMOTE and ROSE wereconsidered but had similar performance as down sampling with highercomputational requirements.

Multiple modeling approaches were evaluated to build the risk predictionmodel including logistic regression, XGBoost and Random Forest. Therandom forest-based models were independently trained to predict 1, 3and 5-year risk of future lung cancer in smokers enrolled in the NLST.The model strategy was based on a Random Forest approach to develop andoptimize decision trees in the training data to predict a desiredoutcome. This approach was used rather than selecting a fixed thresholdfor a certain burden of disease (i.e. >10% emphysema or >10%interstitial change) because there are almost certainly severalcombinations of emphysema, interstitial change and sarcopenia thatidentify a heightened risk of lung cancer. For example, a smoker having25% of their lung with emphysema may have the same heightened risk forfuture lung cancer as the smoker with 2% emphysema, 12% interstitialchange and decreased pectoralis muscle area. These models were thenmodified to enable prediction of incident (new) cancer rather than justidentify those with prevalent (already present) cancer on the CT scan.This final step leveraged visual data describing features of any lungnodules in the CT image. Although the exemplary embodiments show anddescribe random forest-based models and gradient boosted models, it willbe understood by those of skill in the art that other modelingapproaches such as, for example, logistic regression may also beutilized. It will also be understood by those of skill in the art thatalthough the risk prediction models are specifically shown and describedas providing 1, 3 and 5 year risk predictions, prediction terms may bevaried, as desired.

To build the risk prediction model, the cohort was split 50/50 intotraining and testing groups. Models were trained on the testing groupwith tuning performed using 10 fold cross validation repeated threetimes. Dichotomization of predicted probabilities was performed bymaximizing F-score in the cross-sampled training cohort. Outcomesmodeled included the diagnosis of cancer at 1, 3 and 5 years.Performance measures and visualization include presentation of ROCcurves and enrichment for all time points (as described in the Examplesbelow). The cumulative incidence function and the demographics of theenriched and non-enriched cohort based on 3 year cancer prediction arealso predicted/shown in the Examples below.

Models were constructed to predict future risk of cancer for either theentire cohort of patients or a subgroup of patients. Subgroups ofpatients were categorized based on nodules which were at baselinerated/categorized as Lung-RADS <4B, <4A, <3 and <2. Lung-RADS <4Bincludes patients categorized as Lung-RADS 1-4A. Lung-RADS <4A includespatients categorized as Lung_RADS 1-3. Lung-RADS <3 includes patientscategorized as Lung_RADS 1-2. Lung-Rads <2 includes patients categorizedas Lung_RADS 1. Table 1 summarizes the characteristics for differentLung-RADS classifications. Demographic information and patientcharacteristics of the full cohort (including Lung-RADS classification)is shown in Table 2.

Separate risk prediction models were created for each subgroup ofpatients. That is, the performance specified is not the performance ofthe model of the entire cohort simply applied to the stated subgroup,but rather it is the performance of a subgroup specific model that wastrained on patients of that subgroup.

As shown in the Examples below, all results represent theperformance/findings based on models developed/trained in the trainingcohort and then applied to the testing cohort. In the enrichment tables,the risk prediction model for each time horizon (1 year, 3 year, and 5year) was trained separately as the most important features for 1 yearcancer risk are not necessarily the same as those for 3 and 5 yearcancer risk. This means that the cumulative incidence at 3 years, forexample, is not just the cumulative incidence from the row above (1year) plus the interval number of cases.

When interpreting the cumulative incidence function plots, the rawprobabilities on the y axis are affected significantly by changes in therisk set due to censoring and death. These plots are included primarilyto demonstrate the change in their shape by subgroup. Of particular noteis the fact that with the exclusion of larger/more concerning nodules,there is a less abrupt rise in cancer diagnosis in the first year.

When reviewing the receiver operating characteristic (ROC) curves andthe area under the curve (AUC) values, note that these are primarilyincluded for reference given their familiarity and use in theliterature. Because the models were tuned (e.g., referred to as “tunedrisk prediction model”) to the area under the precision recall curve,the area under the ROC does not improve significantly with the tuningprocess.

When considering potential eligibility for enrollment, note that basedon Lung-RADS criteria, those individuals with 4A and 4B nodules willwarrant early evaluation. However, a significant percentage of both, andespecially 4A, will not be prevalent cancers and therefore may bepotentially able to be enrolled in a potential study of future incidentcancer.

The relative enrollment ratios and percentages are the CT screen toenroll ratio of that Lung-RADS subgroup. The absolute enrollmentpercentage is based on the size of the overall cohort.

Example 2: Predicting Future Risk for Lung-RADS 1-4B

Three separate models were constructed using the full patient cohortusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-4B risk prediction model, the second model is a3 year, Lung-RADS 1-4B risk prediction model, and the third model is a 5year, Lung-RADS 1-4B risk prediction model. For each risk predictionmodel, a training set (e.g., 50% of the cohort) was used to train therisk prediction model and a testing set (e.g., other 50% of the cohort)was used to test the risk prediction model.

The top 10 important features for each of the 1 year, 3 year, and 5 yearrisk prediction models is shown in Table 3. Notably, the majority ofimportant features of the 1 year risk prediction model includenodule-specific features whereas the majority of important features ofthe 3 year and 5 year risk prediction model are objective features(e.g., non-nodule specific features such as features of the body and/orlung parenchyma).

Notably, for the 1 year, Lung-RADS 1-4B prediction model, the top threefeatures in terms of feature importance are nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-4B prediction model are nodule specificfeatures. Additionally, 6 of the top 10 features in terms of featureimportance for the 1 year, Lung-RADS 1-4B prediction model arenon-nodule specific features. For the 3 year, Lung-RADS 1-4B predictionmodel, 2 of the top 3 features in terms of feature importance are nodulespecific features. Additionally, 3 of the top 5 features in terms offeature importance for a 3 year, Lung-RADS 1-4B prediction model arenodule specific features. Additionally, 6 of the top 10 features interms of feature importance for a 3 year, Lung-RADS 1-4B predictionmodel are non-nodule specific features. For the 5 year, Lung-RADS 1-4Bprediction model, 2 of the top 3 features in terms of feature importanceare nodule specific features. Additionally, 3 of the top 5 features interms of feature importance for a 5 year, Lung-RADS 1-4B predictionmodel are non-nodule specific features. Additionally, 7 of the top 10features in terms of feature importance for a 5 year, Lung-RADS 1-4Bprediction model are non-nodule specific features.

FIG. 5A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across the Lung-RADS 1-4B ofpatients. The base random forest risk prediction model exhibited an AUCvalue of 0.92 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.90. FIG. 5B depicts performance of a riskprediction model for predicting likelihood of cancer within 3 yearsacross the Lung-RADS 1-4B of patients. The base random forest riskprediction model exhibited AUC value of 0.79 whereas the tuned randomforest risk prediction model exhibited an AUC value of 0.79.Additionally, Table 4 documents the characteristics of the Lung-RADS1-4B of patients according to the cancer prediction determined by the 3year risk prediction model.

FIG. 5C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across the Lung-RADS 1-4B ofpatients. The base random forest risk prediction model exhibited AUCvalue of 0.74 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.74. Altogether, the results of FIGS. 5A-5Cindicate that different risk prediction models can be constructed anddeployed to predict likelihood of cancer in Lung-RADS 1-4Bs of patientsacross different future horizon timepoints.

FIG. 5D depicts the 3 year cumulative incidence function across theLung-RADS 1-4B of patients. Here, the risk prediction model predictedcancer for 450 patients and no cancer for the other 6643 patients. Giventhat that this includes the Lung-RADS 1-4B of patients (which includesthe most at risk lung cancer nodules e.g., Lung-RADS 4A/4B), thecumulative incidence function for the 450 predicted cancer patientsreflects the higher rate of incidence in the early months (e.g., between0 and 6 months) in comparison to a lower rate of incidence in subsequentyears.

Table 5 depicts the enrichment results of the full patient cohort usingthe future cancer predictions from the 1 year, 3 year, or 5 year riskprediction models. Specifically, Table 5 shows the enrichment results ofthe risk prediction models in comparison to background rate of cancersin the original cohort (referred to in Table 5 as “Null Model”). Theapplication of the risk prediction models significantly improves thecumulative incidence (last column of Table 5).

Specifically, for the 1 year model, the background rate (“Null Model”)has a cumulative incidence of 1.79 (e.g., 1.79% of patients in thecohort are diagnosed with cancer within 1 year). Applying the 1 yearrisk prediction model (“Random Forest”) enables patient enrichment,which achieves a cumulative incidence of 19.72 (e.g., 19.72% of patientsincluded in the cohort due to the prediction of the 1 year riskprediction model are diagnosed with cancer within 1 year). Thus, theapplication of the 1 year risk prediction model achieves a 11-foldincrease in cumulative incidence.

For the 3 year model, the background rate (“Null Model”) has acumulative incidence of 4.2 (e.g., 4.2% of patients in the cohort arediagnosed with cancer within 3 years). Applying the 3 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 26.44 (e.g., 26.44% of patientsincluded in the cohort due to the prediction of the 3 year riskprediction model are diagnosed with cancer within 3 years). Thus, theapplication of the 3 year risk prediction model achieves a 6.3-foldincrease in cumulative incidence.

For the 5 year model, the background rate (“Null Model”) has acumulative incidence of 5.58 (e.g., 5.58% of patients in the cohort arediagnosed with cancer within 5 years). Applying the 5 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 28.43 (e.g., 28.43% of patientsincluded in the cohort due to the prediction of the 5 year riskprediction model are diagnosed with cancer within 5 years). Thus, theapplication of the 5 year risk prediction model achieves a 5.1-foldincrease in cumulative incidence.

Altogether, Table 5 indicates that the various risk prediction modelscan be implemented for enriching Lung-RADS 1-4B patients, therebyreducing the number of patients that need to be enrolled in clinicaltrials.

Example 3: Predicting Future Risk for Lung-RADS 1-4A Patients

Three separate models were constructed using the Lung-RADS 1-4A patientsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-4A risk prediction model, the second model is a3 year, Lung-RADS 1-4A risk prediction model, and the third model is a 5year, Lung-RADS 1-4A risk prediction model. For each risk predictionmodel, a training set (e.g., 50% of the cohort) was used to train therisk prediction model and a testing set (e.g., other 50% of the cohort)was used to test the risk prediction model.

The top 10 important features for each of the 1 year, 3 year, and 5 yearrisk prediction models is shown in Table 6. Notably, 5 of the top 10features of the 1 year risk prediction model include nodule-specificfeatures whereas the majority of important features of the 3 year and 5year risk prediction model are objective features (e.g., non-nodulespecific features such as features of the body and/or lung parenchyma).

Specifically, for the 1 year, Lung-RADS 1-4A prediction model, the top 3features in terms of feature importance are nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-4A prediction model are nodule specificfeatures. Additionally, 5 of the top 10 features in terms of featureimportance for the 1 year, Lung-RADS 1-4A prediction model are nodulespecific features.

For the 3 year, Lung-RADS 1-4A prediction model, 2 of the top 3 featuresin terms of feature importance are nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance forthe 3 year, Lung-RADS 1-4A prediction model are nodule specificfeatures. Additionally, 5 of the top 10 features in terms of featureimportance for the 3 year, Lung-RADS 1-4A prediction model are nodulespecific features.

For the 5 year, Lung-RADS 1-4A prediction model, 2 of the top 3 featuresin terms of feature importance are nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 5 year, Lung-RADS 1-4A prediction model are non-nodule specificfeatures. Additionally, 8 of the top 10 features in terms of featureimportance for the 5 year, Lung-RADS 1-4A prediction model arenon-nodule specific features.

FIG. 6A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-4A patients. Thebase random forest risk prediction model exhibited an AUC value of 0.81whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.85. FIG. 6B depicts performance of a risk prediction modelfor predicting likelihood of cancer within 3 years across Lung-RADS 1-4Apatients. The base random forest risk prediction model exhibited AUCvalue of 0.73 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.72. Additionally, Table 7 documents thecharacteristics of the Lung-RADS 1-4A cohort of patients according tothe cancer prediction determined by the 3 year risk prediction model.

FIG. 6C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-4A patients. Thebase random forest risk prediction model exhibited AUC value of 0.73whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.72. Altogether, the results of FIGS. 6A-6C indicate thatdifferent risk prediction models can be constructed and deployed topredict likelihood of cancer in Lung-RADS 1-4A patient cohorts acrossdifferent future horizon timepoints. In other words, even though thehighest at-risk patients (e.g., Lung-RADS 4B) have been removed, therisk prediction model is still able to accurately predict likelihood offuture risk of cancer in the lower risk patients (e.g., Lung-RADS 1-4A).

FIG. 6D depicts the 3 year cumulative incidence function acrossLung-RADS 1-4A patients. Here, the risk prediction model predictedcancer for 392 patients and no cancer for the other 6531 patients. Giventhat the cohort of patients excludes Lung-RADS 4B patients, incomparison to Example 2 (shown in FIG. 5D), the cumulative incidencefunction for the 392 predicted cancer patients shown in FIG. 6D reflectsa lower rate of incidence (e.g., fewer prevalent cancers or fewercancers that were present at the time of the T0 CT scan) in the earlymonths (e.g., between 0 and 6 months).

Table 8 depicts the enrichment results of the Lung-RADS 1-4A patientcohort using the future cancer predictions from the 1 year, 3 year, or 5year risk prediction models. Specifically, Table 8 shows the enrichmentresults of the risk prediction models in comparison to background rateof cancers in the original cohort (referred to in Table 8 as “NullModel”). The application of the risk prediction models significantlyimproves the cumulative incidence (last column of Table 8).

Specifically, for the 1 year model, the background rate (“Null Model”)has a cumulative incidence of 0.98 (e.g., 0.98% of patients in thecohort are diagnosed with cancer within 1 year). Applying the 1 yearrisk prediction model (“Random Forest”) enables patient enrichment,which achieves a cumulative incidence of 7.48 (e.g., 7.48% of patientsincluded in the cohort due to the prediction of the 1 year riskprediction model are diagnosed with cancer within 1 year). Thus, theapplication of the 1 year risk prediction model achieves a 7.6-foldincrease in cumulative incidence.

For the 3 year model, the background rate (“Null Model”) has acumulative incidence of 3.28 (e.g., 3.28% of patients in the cohort arediagnosed with cancer within 3 years). Applying the 3 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 16.84 (e.g., 26.44% of patientsincluded in the cohort due to the prediction of the 3 year riskprediction model are diagnosed with cancer within 3 years). Thus, theapplication of the 3 year risk prediction model achieves a 5.1-foldincrease in cumulative incidence.

For the 5 year model, the background rate (“Null Model”) has acumulative incidence of 4.65 (e.g., 4.65% of patients in the cohort arediagnosed with cancer within 5 years). Applying the 5 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 17.97 (e.g., 17.97% of patientsincluded in the cohort due to the prediction of the 5 year riskprediction model are diagnosed with cancer within 5 years). Thus, theapplication of the 5 year risk prediction model achieves a 3.9-foldincrease in cumulative incidence.

Altogether, Table 8 indicates that the various future risk predictionmodels can be implemented for enriching patients in the Lung-RADS 1-4Acohort, thereby reducing the number of patients that need to be enrolledin clinical trials.

Example 4: Predicting Future Risk for Lung-RADS 1-3 Patients

Three separate models were constructed using the Lung-RADS 1-3 patientsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-3 risk prediction model, the second model is a3 year, Lung-RADS 1-3 risk prediction model, and the third model is a 5year, Lung-RADS 1-3 risk prediction model. For each risk predictionmodel, a training set (e.g., 50% of the cohort) was used to train therisk prediction model and a testing set (e.g., other 50% of the cohort)was used to test the risk prediction model.

The top 10 important features for each of the 1 year, 3 year, and 5 yearrisk prediction models is shown in Table 9. Notably, the majority ofimportant features of the 1 year risk prediction model includenodule-specific features whereas the majority of important features ofthe 3 year and 5 year risk prediction model are objective features(e.g., non-nodule specific features such as features of the body and/orlung parenchyma).

Notably, for the 1 year, Lung-RADS 1-3 prediction model, 2 of the top 3features in terms of feature importance are nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures. Additionally, 7 of the top 10 features in terms of featureimportance for the 1 year, Lung-RADS 1-3 prediction model are non-nodulespecific features.

For the 3 year, Lung-RADS 1-3 prediction model, 2 of the top 3 featuresin terms of feature importance are non-nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance forthe 3 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures. Additionally, 8 of the top 10 features in terms of featureimportance for the 3 year, Lung-RADS 1-3 prediction model are non-nodulespecific features.

For the 5 year, Lung-RADS 1-3 prediction model, 2 of the top 3 featuresin terms of feature importance are non-nodule specific features.Additionally, 4 of the top 5 features in terms of feature importance forthe 5 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures. Additionally, 9 of the top 10 features in terms of featureimportance for the 5 year, Lung-RADS 1-3 prediction model are non-nodulespecific features.

FIG. 7A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-3 patients. Thebase random forest risk prediction model exhibited an AUC value of 0.82whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.82. FIG. 7B depicts performance of a risk prediction modelfor predicting likelihood of cancer within 3 years across Lung-RADS 1-3patients. The base random forest risk prediction model exhibited AUCvalue of 0.72 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.73. Additionally, Table 10 documents thecharacteristics of the Lung-RADS 1-3 patient cohort according to thecancer prediction determined by the 3 year risk prediction model.

FIG. 7C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-3 patients. Thebase random forest risk prediction model exhibited AUC value of 0.67whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.68. Altogether, the results of FIGS. 7A-7C indicate thatdifferent risk prediction models can be constructed and deployed topredict likelihood of cancer in Lung-RADS 1-3 patient cohorts acrossdifferent future horizon timepoints. In other words, even though thehighest at-risk patients (e.g., Lung-RADS 4A/4B) have been removed, therisk prediction model is still able to accurately predict likelihood offuture risk of cancer in the lower risk patients (e.g., Lung-RADS 1-3).

FIG. 7D depicts the 3 year cumulative incidence function acrossLung-RADS 1-3 patients. Here, the risk prediction model predicted cancerfor 324 patients and no cancer for the other 6085 patients. Given thatthe cohort of patients excludes both Lung-RADS 4A and 4B patients, theremoval of the 4A and 4B patients manifests as a lower incidence ofcancer in the 324 predicted cancer patients in the early months (e.g.,between 0 and 6 months) in comparison to predicted cancer patients inExample 2 shown in FIG. 5D. Specifically, the cumulative incidence atthe 1 year mark for the Lung-RADS 1-3 patient cohort is less than 0.1(shown in FIG. 7D) whereas the cumulative incidence at the 1 year markfor the full patient cohort (e.g., Lung-RADS 1-4B) is ˜0.2.

Table 11 depicts the enrichment results of the Lung-RADS 1-3 patientcohort using the future cancer predictions from the 1 year, 3 year, or 5year risk prediction models. Specifically, Table 11 shows the enrichmentresults of the risk prediction models in comparison to background rateof cancers in the original cohort (referred to in Table 11 as “NullModel”). The application of the risk prediction models significantlyimproves the cumulative incidence (last column of Table 11).

Specifically, for the 1 year model, the background rate (“Null Model”)has a cumulative incidence of 0.70 (e.g., 0.70% of patients in thecohort are diagnosed with cancer within 1 year). Applying the 1 yearrisk prediction model (“Random Forest”) enables patient enrichment,which achieves a cumulative incidence of 6.03 (e.g., 6.03% of patientsincluded in the cohort due to the prediction of the 1 year riskprediction model are diagnosed with cancer within 1 year). Thus, theapplication of the 1 year risk prediction model achieves a 8.6-foldincrease in cumulative incidence.

For the 3 year model, the background rate (“Null Model”) has acumulative incidence of 2.73 (e.g., 2.73% of patients in the cohort arediagnosed with cancer within 3 years). Applying the 3 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 15.43 (e.g., 15.43% of patientsincluded in the cohort due to the prediction of the 3 year riskprediction model are diagnosed with cancer within 3 years). Thus, theapplication of the 3 year risk prediction model achieves a 5.7-foldincrease in cumulative incidence.

For the 5 year model, the background rate (“Null Model”) has acumulative incidence of 4.06 (e.g., 4.06% of patients in the cohort arediagnosed with cancer within 5 years). Applying the 5 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 14.88 (e.g., 14.88% of patientsincluded in the cohort due to the prediction of the 5 year riskprediction model are diagnosed with cancer within 5 years). Thus, theapplication of the 5 year risk prediction model achieves a 3.7-foldincrease in cumulative incidence.

Altogether, Table 11 indicates that the various risk prediction modelscan be implemented for enriching patients in the Lung-RADS 1-3 cohort,thereby reducing the number of patients that need to be enrolled inclinical trials.

Example 5: Predicting Future Risk for Lung-RADS 1-2 Patients

Three separate models were constructed using the Lung-RADS 1-2 patientsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-2 risk prediction model, the second model is a3 year risk, Lung-RADS 1-2 prediction model, and the third model is a 5year risk, Lung-RADS 1-2 prediction model. For each risk predictionmodel, a training set (e.g., 50% of the cohort) was used to train therisk prediction model and a testing set (e.g., other 50% of the cohort)was used to test the risk prediction model.

The top 10 important features for each of the 1 year, 3 year, and 5 yearrisk prediction models is shown in Table 12. Notably, the majority ofimportant features of the 1 year risk prediction model includenodule-specific features whereas the majority of important features ofthe 3 year and 5 year risk prediction model are objective features(e.g., non-nodule specific features such as features of the body and/orlung parenchyma).

Notably, for the 1 year, Lung-RADS 1-2 prediction model, 2 of the top 3features in terms of feature importance are nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-2 prediction model are non-nodule specificfeatures. Additionally, 8 of the top 10 features in terms of featureimportance for the 1 year, Lung-RADS 1-2 prediction model are non-nodulespecific features.

For the 3 year, Lung-RADS 1-2 prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, the top 5 features in terms of feature importance for the3 year, Lung-RADS 1-2 prediction model are non-nodule specific features.Additionally, 8 of the top 10 features in terms of feature importancefor the 3 year, Lung-RADS 1-2 prediction model are non-nodule specificfeatures.

For the 5 year, Lung-RADS 1-2 prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, the top 5 features in terms of feature importance for the5 year, Lung-RADS 1-2 prediction model are non-nodule specific features.Additionally, the top 10 features in terms of feature importance for the5 year, Lung-RADS 1-2 prediction model are non-nodule specific features.

FIG. 8A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1-2 patients. Thebase random forest risk prediction model exhibited an AUC value of 0.73whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.72. FIG. 8B depicts performance of a risk prediction modelfor predicting likelihood of cancer within 3 years across Lung-RADS 1-2patients. The base random forest risk prediction model exhibited AUCvalue of 0.66 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.65. Additionally, Table 13 documents thecharacteristics of the Lung-RADS 1-2 patient cohort according to thecancer prediction determined by the 3 year risk prediction model.

FIG. 8C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1-2 patients. Thebase random forest risk prediction model exhibited AUC value of 0.65whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.65. Altogether, the results of FIGS. 8A-8C indicate thatdifferent risk prediction models can be constructed and deployed topredict likelihood of cancer in Lung-RADS 1-2 patient cohorts acrossdifferent future horizon timepoints. In other words, even though thehigher at-risk patients (e.g., Lung-RADS 3/4A/4B) have been removed, therisk prediction model is still able to accurately predict likelihood offuture risk of cancer in the low risk patients (e.g., Lung-RADS 1-2).Notably, as shown in Table 1, the prevalence of Lung-RADS 1-2individuals is ˜90% of the population. Thus, risk prediction models thatare able to predict likelihood of cancer for Lung-RADS 1-2 patientcohorts is highly valuable as it can be applied to a large majority ofthe patient population.

FIG. 8D depicts the 3 year cumulative incidence function acrossLung-RADS 1-2 patients. Here, the risk prediction model predicted cancerfor 294 patients and no cancer for the other 5314 patients. Given thatthe cohort of patients excludes Lung-RADS 3, 4A, and 4B patients, theremoval of the Lung-RADS 3, 4A, and 4B patients manifests as a lowerincidence of cancer in the 294 predicted cancer patients in the earlymonths (e.g., between 0 and 6 months) in comparison to Example 2 shownin FIG. 5D. Specifically, the cumulative incidence at the 1 year markfor the Lung-RADS 1-2 patient cohort is ˜0.025 (shown in FIG. 8D)whereas the cumulative incidence at the 1 year mark for the full patientcohort (e.g., Lung-RADS 1-4B) is ˜0.2.

Table 14 depicts the enrichment results of the Lung-RADS 1-2 patientcohort using the future cancer predictions from the 1 year, 3 year, or 5year risk prediction models. Specifically, Table 14 shows the enrichmentresults of the risk prediction models in comparison to background rateof cancers in the original cohort (referred to in Table 14 as “NullModel”). The application of the risk prediction models significantlyimproves the cumulative incidence (last column of Table 14).

Specifically, for the 1 year model, the background rate (“Null Model”)has a cumulative incidence of 0.43 (e.g., 0.43% of patients in thecohort are diagnosed with cancer within 1 year). Applying the 1 yearrisk prediction model (“Random Forest”) enables patient enrichment,which achieves a cumulative incidence of 2.37 (e.g., 2.37% of patientsincluded in the cohort due to the prediction of the 1 year riskprediction model are diagnosed with cancer within 1 year). Thus, theapplication of the 1 year risk prediction model achieves a 5.5-foldincrease in cumulative incidence.

For the 3 year model, the background rate (“Null Model”) has acumulative incidence of 2.37 (e.g., 2.37% of patients in the cohort arediagnosed with cancer within 3 years). Applying the 3 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 7.14 (e.g., 7.14% of patientsincluded in the cohort due to the prediction of the 3 year riskprediction model are diagnosed with cancer within 3 years). Thus, theapplication of the 3 year risk prediction model achieves a 3-foldincrease in cumulative incidence.

For the 5 year model, the background rate (“Null Model”) has acumulative incidence of 3.67 (e.g., 3.67% of patients in the cohort arediagnosed with cancer within 5 years). Applying the 5 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 7.06 (e.g., 7.06% of patientsincluded in the cohort due to the prediction of the 5 year riskprediction model are diagnosed with cancer within 5 years). Thus, theapplication of the 5 year risk prediction model achieves a 1.9-foldincrease in cumulative incidence.

Altogether, Table 14 indicates that the various risk prediction modelscan be implemented for enriching patients in the Lung-RADS 1-2 cohort,thereby reducing the number of patients that need to be enrolled inclinical trials.

Example 6: Predicting Future Risk for Lung-RADS 1 Patients

Three separate models were constructed using the Lung-RADS 1 patientsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1 risk prediction model, the second model is a 3year, Lung-RADS 1 risk prediction model, and the third model is a 5 yearrisk, Lung-RADS 1 prediction model. For each risk prediction model, atraining set (e.g., 50% of the cohort) was used to train the riskprediction model and a testing set (e.g., other 50% of the cohort) wasused to test the risk prediction model.

The top 10 important features for each of the 1 year, 3 year, and 5 yearrisk prediction models is shown in Table 15. Notably, the majority ofimportant features of the 1 year risk prediction model includenodule-specific features whereas the majority of important features ofthe 3 year and 5 year risk prediction model are objective features(e.g., non-nodule specific features such as features of the body and/orlung parenchyma).

Notably, for the 1 year, Lung-RADS 1 prediction model, the top 3features in terms of feature importance are non-nodule specificfeatures. Additionally, the top 5 features in terms of featureimportance for the 1 year, Lung-RADS 1 prediction model are non-nodulespecific features.

For the 3 year, Lung-RADS 1 prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, the top 5 features in terms of feature importance for the3 year, Lung-RADS 1 prediction model are non-nodule specific features.Additionally, the top 10 features in terms of feature importance for the3 year, Lung-RADS 1 prediction model are non-nodule specific features.

For the 5 year, Lung-RADS 1 prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, the top 5 features in terms of feature importance for the5 year, Lung-RADS 1 prediction model are non-nodule specific features.Additionally, the top 10 features in terms of feature importance for the5 year, Lung-RADS 1 prediction model are non-nodule specific features.

FIG. 9A depicts performance of a risk prediction model for predictinglikelihood of cancer within 1 year across Lung-RADS 1 patients. The baserandom forest risk prediction model exhibited an AUC value of 0.57whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.63. FIG. 9B depicts performance of a risk prediction modelfor predicting likelihood of cancer within 3 years across Lung-RADS 1patients. The base random forest risk prediction model exhibited AUCvalue of 0.63 whereas the tuned random forest risk prediction modelexhibited an AUC value of 0.60. Additionally, Table 16 documents thecharacteristics of the Lung-RADS 1 patient cohort according to thecancer prediction determined by the 3 year risk prediction model.

FIG. 9C depicts performance of a risk prediction model for predictinglikelihood of cancer within 5 years across Lung-RADS 1 patients. Thebase random forest risk prediction model exhibited AUC value of 0.57whereas the tuned random forest risk prediction model exhibited an AUCvalue of 0.61. Altogether, the results of FIGS. 9A-9C indicate thatdifferent risk prediction models can be constructed and deployed topredict likelihood of cancer in Lung-RADS 1 patient cohorts acrossdifferent future horizon timepoints.

FIG. 9D depicts the 3 year cumulative incidence function acrossLung-RADS 1 patients. Here, the risk prediction model predicted cancerfor 261 patients and no cancer for the other 4077 patients. Here, thecohort of patients only includes Lung-RADS 1 patients, which arepatients are at lowest risk of developing cancer in the immediatefuture. As shown in FIG. 9D, the cumulative incidence at the 1 year markfor the 261 predicted cancer patients of the Lung-RADS 1 patient cohortis less than 0.02 whereas the cumulative incidence at the 1 year markfor the full patient cohort (e.g., Lung-RADS 1-4B) is ˜0.2.

Table 17 depicts the enrichment results of the Lung-RADS 1 patientcohort using the future cancer predictions from the 1 year, 3 year, or 5year risk prediction models. Specifically, Table 17 shows the enrichmentresults of the risk prediction models in comparison to background rateof cancers in the original cohort (referred to in Table 17 as “NullModel”). The application of the risk prediction models significantlyimproves the cumulative incidence (last column of Table 17).

Specifically, for the 3 year model, the background rate (“Null Model”)has a cumulative incidence of 2.31 (e.g., 2.31% of patients in thecohort are diagnosed with cancer within 3 years). Applying the 3 yearrisk prediction model (“Random Forest”) enables patient enrichment,which achieves a cumulative incidence of 5.36 (e.g., 5.36% of patientsincluded in the cohort due to the prediction of the 3 year riskprediction model are diagnosed with cancer within 3 years). Thus, theapplication of the 3 year risk prediction model achieves a 2.3-foldincrease in cumulative incidence.

For the 5 year model, the background rate (“Null Model”) has acumulative incidence of 3.67 (e.g., 3.67% of patients in the cohort arediagnosed with cancer within 5 years). Applying the 5 year riskprediction model (“Random Forest”) enables patient enrichment, whichachieves a cumulative incidence of 6.80 (e.g., 6.80% of patientsincluded in the cohort due to the prediction of the 5 year riskprediction model are diagnosed with cancer within 5 years). Thus, theapplication of the 5 year risk prediction model achieves a 1.9-foldincrease in cumulative incidence.

Altogether, Table 17 indicates that the various risk prediction modelscan be implemented for enriching patients in the Lung-RADS 1 cohort,thereby reducing the number of patients that need to be enrolled inclinical trials.

Example 7: Predicting Future Risk of Cancer for Lung-RADS 1-4B SubjectsUsing Radiomic Features

Two separate models were constructed using the Lung-RADS 1-4B subjectsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-4B risk prediction model and the second modelis a 3 year, Lung-RADS 1-4B risk prediction model. For each riskprediction model, a training set (e.g., 50% of the cohort) was used totrain the risk prediction model and a testing set (e.g., other 50% ofthe cohort) was used to test the risk prediction model. The performanceof 1 year Lung-RADS 1-4B and 3 year Lung-RADS 1-4B risk predictionmodels are shown in Table 18.

The top 10 important features (e.g., nodule specific features includingradiomic features and non-nodule specific features) for each of the 1year and 3 year risk prediction models are shown in Table 19. Notably,for the 1 year, Lung-RADS 1-4B prediction model, 2 of the top 3 featuresin terms of feature importance are nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-4B prediction model are nodule specificfeatures.

For the 3 year, Lung-RADS 1-4B prediction model, the top 3 features interms of feature importance are nodule specific features. Additionally,the top 5 features in terms of feature importance for the 3 year,Lung-RADS 1-4B prediction model are nodule specific features.

FIG. 10A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-4B patients. The XGboost risk prediction modelexhibited an AUC value of 0.889. FIG. 10B depicts performance of a riskprediction model incorporating radiomic features for predictinglikelihood of cancer within 3 years across Lung-RADS 1-4B patients. TheXGboost risk prediction model exhibited an AUC value of 0.792.

Example 8: Predicting Future Risk of Cancer for Lung-RADS 1-4A SubjectsUsing Radiomic Features

Two separate models were constructed using the Lung-RADS 1-4A subjectsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-4A risk prediction model and the second modelis a 3 year, Lung-RADS 1-4A risk prediction model. For each riskprediction model, a training set (e.g., 50% of the cohort) was used totrain the risk prediction model and a testing set (e.g., other 50% ofthe cohort) was used to test the risk prediction model. The performanceof 1 year Lung-RADS 1-4A and 3 year Lung-RADS 1-4A risk predictionmodels are shown in Table 18.

The top 10 important features (e.g., nodule specific features includingradiomic features and non-nodule specific features) for each of the 1year and 3 year risk prediction models are shown in Table 20. Notably,for the 1 year, Lung-RADS 1-4A prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-4A prediction model are non-nodule specificfeatures.

For the 3 year, Lung-RADS 1-4A prediction model, the top 3 features interms of feature importance are nodule specific features. Additionally,4 of the top 5 features in terms of feature importance for the 3 year,Lung-RADS 1-4A prediction model are nodule specific features.

FIG. 11A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-4A patients. The XGboost risk prediction modelexhibited an AUC value of 0.779. FIG. 11B depicts performance of a riskprediction model incorporating radiomic features for predictinglikelihood of cancer within 3 years across Lung-RADS 1-4A patients. TheXGboost risk prediction model exhibited an AUC value of 0.696.

Example 9: Predicting Future Risk of Cancer for Lung-RADS 1-3 SubjectsUsing Radiomic Features

Two separate models were constructed using the Lung-RADS 1-3 subjectsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 1-3 risk prediction model and the second model isa 3 year, Lung-RADS 1-3 risk prediction model. For each risk predictionmodel, a training set (e.g., 50% of the cohort) was used to train therisk prediction model and a testing set (e.g., other 50% of the cohort)was used to test the risk prediction model. The performance of 1 yearLung-RADS 1-3 and 3 year Lung-RADS 1-3 risk prediction models are shownin Table 18.

The top 10 important features (e.g., nodule specific features includingradiomic features and non-nodule specific features) for each of the 1year and 3 year risk prediction models are shown in Table 21. Notably,for the 1 year, Lung-RADS 1-3 prediction model, the top 3 features interms of feature importance are non-nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 1 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures.

For the 3 year, Lung-RADS 1-3 prediction model, 2 of the top 3 featuresin terms of feature importance are non-nodule specific features.Additionally, 3 of the top 5 features in terms of feature importance forthe 3 year, Lung-RADS 1-3 prediction model are non-nodule specificfeatures.

FIG. 12A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 1-3 patients. The XGboost risk prediction modelexhibited an AUC value of 0.793. FIG. 12B depicts performance of a riskprediction model incorporating radiomic features for predictinglikelihood of cancer within 3 years across Lung-RADS 1-3 patients. TheXGboost risk prediction model exhibited an AUC value of 0.676.

Example 10: Predicting Future Risk of Cancer for Lung-RADS 2-4B SubjectsUsing Radiomic Features

Two separate models were constructed using the Lung-RADS 2-4B subjectsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 2-4B risk prediction model and the second modelis a 3 year, Lung-RADS 2-4B risk prediction model. For each riskprediction model, a training set (e.g., 50% of the cohort) was used totrain the risk prediction model and a testing set (e.g., other 50% ofthe cohort) was used to test the risk prediction model. The performanceof 1 year Lung-RADS 2-4B and 3 year Lung-RADS 2-4B risk predictionmodels are shown in Table 18.

The top 10 important features (e.g., nodule specific features includingradiomic features and non-nodule specific features) for each of the 1year and 3 year risk prediction models are shown in Table 22. Notably,for the 1 year, Lung-RADS 2-4B prediction model, the top 3 features interms of feature importance are nodule specific features. Additionally,the top 5 features in terms of feature importance for the 1 year,Lung-RADS 2-4B prediction model are nodule specific features.

For the 3 year, Lung-RADS 2-4B risk prediction model, the top 3 featuresin terms of feature importance are nodule specific features.Additionally, the top 5 features in terms of feature importance for the3 year, Lung-RADS 2-4B risk prediction model are nodule specificfeatures.

FIG. 13A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 2-4B patients. The XGboost risk prediction modelexhibited an AUC value of 0.928. FIG. 13B depicts performance of a riskprediction model incorporating radiomic features for predictinglikelihood of cancer within 3 years across Lung-RADS 2-4B patients. TheXGboost risk prediction model exhibited an AUC value of 0.809.

Example 11: Predicting Future Risk of Cancer for Lung-RADS 4A-4BSubjects Using Radiomic Features

Two separate models were constructed using the Lung-RADS 4A-4B subjectsusing the methods described in Example 1. Specifically, the first modelis a 1 year, Lung-RADS 4A-4B risk prediction model and the second modelis a 3 year, Lung-RADS 4A-4B risk prediction model. For each riskprediction model, a training set (e.g., 50% of the cohort) was used totrain the risk prediction model and a testing set (e.g., other 50% ofthe cohort) was used to test the risk prediction model. The performanceof 1 year Lung-RADS 4A-4B and 3 year Lung-RADS 4A-4B risk predictionmodels are shown in Table 18.

The top 10 important features (e.g., nodule specific features includingradiomic features and non-nodule specific features) for each of the 1year and 3 year risk prediction models are shown in Table 23. Notably,for the 1 year, Lung-RADS 4A-4B prediction model, the top 3 features interms of feature importance are nodule specific features. Additionally,the top 5 features in terms of feature importance for the 1 year,Lung-RADS 4A-4B prediction model are nodule specific features.

For the 3 year, Lung-RADS 4A-4B risk prediction model, the top 3features in terms of feature importance are nodule specific features.Additionally, the top 5 features in terms of feature importance for the3 year, Lung-RADS 4A-4B risk prediction model are nodule specificfeatures.

FIG. 14A depicts performance of a risk prediction model incorporatingradiomic features for predicting likelihood of cancer within 1 yearacross Lung-RADS 4A-4B patients. The XGboost risk prediction modelexhibited an AUC value of 0.91. FIG. 14B depicts performance of a riskprediction model incorporating radiomic features for predictinglikelihood of cancer within 3 years across Lung-RADS 4A-4B patients. TheXGboost risk prediction model exhibited an AUC value of 0.798.

Example 12: Additional Examples of Predicting Future Risk of Cancer forLung-RADS 1-4B Subjects

Additional models are constructed using Lung-RADS 1-4B subjects usingthe methods described in Example 1. For example, such models canincorporate the non-nodule features described in Example 1 as well asnodule specific features (such as radiomic features) described inExample 1.

The additional models are either random forest models or gradientboosted models. Specifically, a first model is a 6 month, Lung-RADS 1-4Brisk prediction model, a second model is a 1.5 year, Lung-RADS 1-4B riskprediction model, a third model is a 2.5 year, Lung-RADS 1-4B riskprediction model, a fourth model is a 4 year, Lung-RADS 1-4B riskprediction model, a fifth model is a 4.5 year, Lung-RADS 1-4B riskprediction model, a sixth model is a 5.5 year, Lung-RADS 1-4B riskprediction model, a seventh model is a 6 year, Lung-RADS 1-4B riskprediction model, an eight model is a 7 year, Lung-RADS 1-4B riskprediction model, a ninth model is a 8 year, Lung-RADS 1-4B riskprediction model, a tenth model is a 9 year, Lung-RADS 1-4B riskprediction model, an eleventh model is a 10 year, Lung-RADS 1-4B riskprediction model, a twelfth model is a 11 year, Lung-RADS 1-4B riskprediction model, a thirteenth model is a 11 year, Lung-RADS 1-4B riskprediction model, a fourteenth model is a 12 year, Lung-RADS 1-4B riskprediction model, a fifteenth model is a 13 year, Lung-RADS 1-4B riskprediction model, a sixteenth model is a 14 year, Lung-RADS 1-4B riskprediction model, a seventeenth model is a 15 year, Lung-RADS 1-4B riskprediction model, an eighteenth model is a 16 year, Lung-RADS 1-4B riskprediction model, a nineteenth model is a 17 year, Lung-RADS 1-4B riskprediction model, a twentieth model is a 18 year, Lung-RADS 1-4B riskprediction model, a twenty-first model is a 19 year, Lung-RADS 1-4B riskprediction model, and a twenty-second model is a 20 year, Lung-RADS 1-4Brisk prediction model.

The additional models exhibit an AUC value of at least 0.55. At leastone of the additional models exhibits an AUC value of at least 0.60. Atleast one of the additional models exhibits an AUC value of at least0.65. At least one of the additional models exhibits an AUC value of atleast 0.70. At least one of the additional models exhibits an AUC valueof at least 0.75. At least one of the additional models exhibits an AUCvalue of at least 0.80.

Example 13: Additional Examples of Predicting Future Risk of Cancer forLung-RADS 1-4A Subjects

Additional models are constructed using Lung-RADS 1-4A subjects usingthe methods described in Example 1. For example, such models canincorporate the non-nodule features described in Example 1 as well asnodule specific features (such as radiomic features) described inExample 1.

The additional models are either random forest models or gradientboosted models. Specifically, a first model is a 6 month, Lung-RADS 1-4Arisk prediction model, a second model is a 1.5 year, Lung-RADS 1-4A riskprediction model, a third model is a 2.5 year, Lung-RADS 1-4A riskprediction model, a fourth model is a 4 year, Lung-RADS 1-4A riskprediction model, a fifth model is a 4.5 year, Lung-RADS 1-4A riskprediction model, a sixth model is a 5.5 year, Lung-RADS 1-4A riskprediction model, a seventh model is a 6 year, Lung-RADS 1-4A riskprediction model, an eight model is a 7 year, Lung-RADS 1-4A riskprediction model, a ninth model is a 8 year, Lung-RADS 1-4A riskprediction model, a tenth model is a 9 year, Lung-RADS 1-4A riskprediction model, an eleventh model is a 10 year, Lung-RADS 1-4A riskprediction model, a twelfth model is a 11 year, Lung-RADS 1-4A riskprediction model, a thirteenth model is a 11 year, Lung-RADS 1-4A riskprediction model, a fourteenth model is a 12 year, Lung-RADS 1-4A riskprediction model, a fifteenth model is a 13 year, Lung-RADS 1-4A riskprediction model, a sixteenth model is a 14 year, Lung-RADS 1-4A riskprediction model, a seventeenth model is a 15 year, Lung-RADS 1-4A riskprediction model, an eighteenth model is a 16 year, Lung-RADS 1-4A riskprediction model, a nineteenth model is a 17 year, Lung-RADS 1-4A riskprediction model, a twentieth model is a 18 year, Lung-RADS 1-4A riskprediction model, a twenty-first model is a 19 year, Lung-RADS 1-4A riskprediction model, and a twenty-second model is a 20 year, Lung-RADS 1-4Arisk prediction model.

The additional models exhibit an AUC value of at least 0.55. At leastone of the additional models exhibits an AUC value of at least 0.60. Atleast one of the additional models exhibits an AUC value of at least0.65. At least one of the additional models exhibits an AUC value of atleast 0.70. At least one of the additional models exhibits an AUC valueof at least 0.75. At least one of the additional models exhibits an AUCvalue of at least 0.80.

Example 14: Additional Examples of Predicting Future Risk of Cancer forLung-RADS 1-3 Subjects

Additional models are constructed using Lung-RADS 1-3 subjects using themethods described in Example 1. For example, such models can incorporatethe non-nodule features described in Example 1 as well as nodulespecific features (such as radiomic features) described in Example 1.

The additional models are either random forest models or gradientboosted models. Specifically, a first model is a 6 month, Lung-RADS 1-3risk prediction model, a second model is a 1.5 year, Lung-RADS 1-3 riskprediction model, a third model is a 2.5 year, Lung-RADS 1-3 riskprediction model, a fourth model is a 4 year, Lung-RADS 1-3 riskprediction model, a fifth model is a 4.5 year, Lung-RADS 1-3 riskprediction model, a sixth model is a 5.5 year, Lung-RADS 1-3 riskprediction model, a seventh model is a 6 year, Lung-RADS 1-3 riskprediction model, an eight model is a 7 year, Lung-RADS 1-3 riskprediction model, a ninth model is a 8 year, Lung-RADS 1-3 riskprediction model, a tenth model is a 9 year, Lung-RADS 1-3 riskprediction model, an eleventh model is a 10 year, Lung-RADS 1-3 riskprediction model, a twelfth model is a 11 year, Lung-RADS 1-3 riskprediction model, a thirteenth model is a 11 year, Lung-RADS 1-3 riskprediction model, a fourteenth model is a 12 year, Lung-RADS 1-3 riskprediction model, a fifteenth model is a 13 year, Lung-RADS 1-3 riskprediction model, a sixteenth model is a 14 year, Lung-RADS 1-3 riskprediction model, a seventeenth model is a 15 year, Lung-RADS 1-3 riskprediction model, an eighteenth model is a 16 year, Lung-RADS 1-3 riskprediction model, a nineteenth model is a 17 year, Lung-RADS 1-3 riskprediction model, a twentieth model is a 18 year, Lung-RADS 1-3 riskprediction model, a twenty-first model is a 19 year, Lung-RADS 1-3 riskprediction model, and a twenty-second model is a 20 year, Lung-RADS 1-3risk prediction model.

The additional models exhibit an AUC value of at least 0.55. At leastone of the additional models exhibits an AUC value of at least 0.60. Atleast one of the additional models exhibits an AUC value of at least0.65. At least one of the additional models exhibits an AUC value of atleast 0.70. At least one of the additional models exhibits an AUC valueof at least 0.75. At least one of the additional models exhibits an AUCvalue of at least 0.80.

Example 15: Additional Examples of Predicting Future Risk of Cancer forLung-RADS 1-2 Subjects

Additional models are constructed using Lung-RADS 1-2 subjects using themethods described in Example 1. For example, such models can incorporatethe non-nodule features described in Example 1 as well as nodulespecific features (such as radiomic features) described in Example 1.

The additional models are either random forest models or gradientboosted models. Specifically, a first model is a 6 month, Lung-RADS 1-2risk prediction model, a second model is a 1.5 year, Lung-RADS 1-2 riskprediction model, a third model is a 2.5 year, Lung-RADS 1-2 riskprediction model, a fourth model is a 4 year, Lung-RADS 1-2 riskprediction model, a fifth model is a 4.5 year, Lung-RADS 1-2 riskprediction model, a sixth model is a 5.5 year, Lung-RADS 1-2 riskprediction model, a seventh model is a 6 year, Lung-RADS 1-2 riskprediction model, an eight model is a 7 year, Lung-RADS 1-2 riskprediction model, a ninth model is a 8 year, Lung-RADS 1-2 riskprediction model, a tenth model is a 9 year, Lung-RADS 1-2 riskprediction model, an eleventh model is a 10 year, Lung-RADS 1-2 riskprediction model, a twelfth model is a 11 year, Lung-RADS 1-2 riskprediction model, a thirteenth model is a 11 year, Lung-RADS 1-2 riskprediction model, a fourteenth model is a 12 year, Lung-RADS 1-2 riskprediction model, a fifteenth model is a 13 year, Lung-RADS 1-2 riskprediction model, a sixteenth model is a 14 year, Lung-RADS 1-2 riskprediction model, a seventeenth model is a 15 year, Lung-RADS 1-2 riskprediction model, an eighteenth model is a 16 year, Lung-RADS 1-2 riskprediction model, a nineteenth model is a 17 year, Lung-RADS 1-2 riskprediction model, a twentieth model is a 18 year, Lung-RADS 1-2 riskprediction model, a twenty-first model is a 19 year, Lung-RADS 1-2 riskprediction model, and a twenty-second model is a 20 year, Lung-RADS 1-2risk prediction model.

The additional models exhibit an AUC value of at least 0.55. At leastone of the additional models exhibits an AUC value of at least 0.60. Atleast one of the additional models exhibits an AUC value of at least0.65. At least one of the additional models exhibits an AUC value of atleast 0.70. At least one of the additional models exhibits an AUC valueof at least 0.75. At least one of the additional models exhibits an AUCvalue of at least 0.80.

Example 15: Additional Examples of Predicting Future Risk of Cancer forLung-RADS 1 Subjects

Additional models are constructed using Lung-RADS 1 subjects using themethods described in Example 1. For example, such models can incorporatethe non-nodule features described in Example 1 as well as nodulespecific features (such as radiomic features) described in Example 1.

The additional models are either random forest models or gradientboosted models. Specifically, a first model is a 6 month, Lung-RADS 1risk prediction model, a second model is a 1.5 year, Lung-RADS 1 riskprediction model, a third model is a 2.5 year, Lung-RADS 1 riskprediction model, a fourth model is a 4 year, Lung-RADS 1 riskprediction model, a fifth model is a 4.5 year, Lung-RADS 1 riskprediction model, a sixth model is a 5.5 year, Lung-RADS 1 riskprediction model, a seventh model is a 6 year, Lung-RADS 1 riskprediction model, an eight model is a 7 year, Lung-RADS 1 riskprediction model, a ninth model is a 8 year, Lung-RADS 1 risk predictionmodel, a tenth model is a 9 year, Lung-RADS 1 risk prediction model, aneleventh model is a 10 year, Lung-RADS 1 risk prediction model, atwelfth model is a 11 year, Lung-RADS 1 risk prediction model, athirteenth model is a 11 year, Lung-RADS 1 risk prediction model, afourteenth model is a 12 year, Lung-RADS 1 risk prediction model, afifteenth model is a 13 year, Lung-RADS 1 risk prediction model, asixteenth model is a 14 year, Lung-RADS 1 risk prediction model, aseventeenth model is a 15 year, Lung-RADS 1 risk prediction model, aneighteenth model is a 16 year, Lung-RADS 1 risk prediction model, anineteenth model is a 17 year, Lung-RADS 1 risk prediction model, atwentieth model is a 18 year, Lung-RADS 1 risk prediction model, atwenty-first model is a 19 year, Lung-RADS 1 risk prediction model, anda twenty-second model is a 20 year, Lung-RADS 1 risk prediction model.

The additional models exhibit an AUC value of at least 0.55. At leastone of the additional models exhibits an AUC value of at least 0.60. Atleast one of the additional models exhibits an AUC value of at least0.65. At least one of the additional models exhibits an AUC value of atleast 0.70. At least one of the additional models exhibits an AUC valueof at least 0.75. At least one of the additional models exhibits an AUCvalue of at least 0.80.

Example 16: Example Future Risk Predictions for Two Patients

The risk prediction models trained using the data from the NLST providea multi-modal risk assessment and enrichment approach. For example,consider two patients who developed cancer within 3 years of enrollmentin NLST. Both had Lung-RADS 3 nodules at baseline, both were predictedto develop cancer within 3 years by the algorithm and both developedstage IA adenocarcinoma within 3 years. In fact, the predictedprobabilities of developing cancer for both patients are within 2% ofeach other (84.8% for Patient 1 and 86.4% for Patient 2). Patient 1 wasa 65-year-old white female former smoker with a 76 pack year smokinghistory, a BMI of 23.5, relatively small pectoralis mass, andsignificant, upper lobe predominant emphysema (31% emphysema by volumebased on local histogram analysis). A 76 pack year smoking history isequivalent to smoking a pack (20) of cigarettes every day for 76 yearsor two packs a day for 38 years. By contrast, Patient 2 was a 59 yearold white male former smoker with a 43 pack year smoking history, a BMIof 29.8, relatively preserved pectoralis mass, and minimal emphysema(2.6% emphysema by volume based on local histogram analysis), butsignificantly more interstitial features (7.1% compared to 4%). In thissecond case, it is this last feature and the strong relationship betweeninterstitial features and cancer that likely leads to the algorithmpredicting the development of cancer within 3 years. The clinical andradiologic differences between these two cases highlight the ability ofthe risk prediction models to identify patients with distinct anddiffering phenotypes who are likely to develop cancer.

All publications, patents, patent applications and other documents citedin this application are hereby incorporated by reference in theirentireties for all purposes to the same extent as if each individualpublication, patent, patent application or other document wereindividually indicated to be incorporated by reference for all purposes.

While various specific embodiments have been illustrated and described,the above specification is not restrictive. It will be appreciated thatvarious changes can be made without departing from the spirit and scopeof the present disclosure(s). Many variations will become apparent tothose skilled in the art upon review of this specification.

Tables

TABLE 1 Summary of Lung-RADS classification Lung- Risk of EstimatedCategory RADS Malig- Population Descriptor score Findings Managementnancy Prevalence Incomplete 0 Prior Additional N/A  1% Chest CT Lungcancer exam- screening ination(s) CT images being and/or located forcomparison comparison. to prior Part or all chest CT of lungsexaminations cannot is needed be evaluated Negative 1 No lung Continue <1% 90% (No nodules or annual nodules nodules with screening andspecific with LDCT definitely calculations: in 12 months benigncomplete, nodules) central, popcorn, concentric rings, and fatcontaining nodules Benign 2 Solid appearance nodules: < or behavior 6mm, (Nodules new < 4 mm with Part solid very low nodules: < likelihood 6mm of total diameter becoming on baseline a clinically screening activeNon solid cancer nodules due to (GGN): < 30 size or lack mm OR ≥ 30 ofgrowth) mm and unchanged or slowly growing Category 3 or 4 nodulesunchanged for ≥ 3 months Probably 3 Solid 6 month  1-2%  5% benignnodules: ≥ LDCT (probably 6 to < 8 benign mm at findings- baseline shortterm OR new 4 follow up mm to < suggested; 6 mm includes Part solidnodules nodules: ≥ with a low 6 mm likelhood total diameter of withsolid becoming component < a clinically 6 mm OR active new < 6 mmcancer) total diameter Non solid nodules (GGN) ≥ 30 mm on baseline CT ornew Probably 4A Solid 3 month 5-15%  2% suspicious nodule(s): ≥ LDCT:(findings 8 to < PET/CT for which 15 mm at may be used additionalbaseline OR when there diagnostic growing < 8 is a ≥ 8 mm testing is mmOR solid recom- new 6 component mended) to < 8 mm Part solid nodules: ≥6 mm with solid component ≥ 6 mm to < 8 mm OR with a new or growing < 4mm solid component Endo- bronchial nodule Suspicious 4B Solid ChestCT >15%  2% (Findings nodule: ≥ with or for which 15 mm OR withoutadditional new or contrast, diagnostic growing PET/CT testing and ≥and/or tissue and/or 8 mm sampling tissue Part solid depending onsampling nodule(s) the is with: a probability of recom- solid malignancymended) component ≥ and 8 mm comorbidities. OR a new or PET/CT growing ≥4 may be used mm solid when there component is a ≥ 8 4X Category 3 mmsolid or 4 nodules component. with For new aditional large nodulesfeatures or that imaging develop on findings that an annual increasesthe repeat suspicion of screening CT, malignancy a 1 month LDCT may berecom- mended to address potentially infectious or inflammatoryconditions

TABLE 2 Overall demographics of full patient cohort DemographicCharacteristics n 7093 Age (Years) (mean 61.7 (5.0)  (SD)) Gender (%)Female 2853 (40.2) Male 4240 (59.8) Race (%) American Indian  14 (0.2)or Alaskan Native Asian 149 (2.1) Black 259 (3.7) Missing/Unknown  13(0.2) More than One  88 (1.2) Race Native Hawaiian  16 (0.2) or OtherPacific Islander White 6534 (92.4) Current Smoking Current Smoker 3670(51.7) Status (%) Former Smoker 3423 (48.3) Pack Years (mean  56.8(24.1) (SD)) Diagnosis with Lung Cancer 298 (4.2) Lung Cancer over 3 NoLung Cancer 6795 (95.8) Years of Follow-up (%) Lung cancer stage StageIA 140 (47.0) (%) Stage IB  30 (10.1) Stage IIA 17 (5.7) Stage IIB 11(3.7) Stage IIIA  33 (11.1) Stage IIIB  6 (2.0) Stage IV  50 (16.8)Unknown/Other 11 (3.7) Lung cancer type Adenocarcinoma 160 (53.7) (%)Squamous cell  59 (19.8) carcinoma Non-small cell 29 (9.7) carcinoma,NOS Small cell carcinoma 25 (8.4) Neuroendocrine tumor 21 (7.0) Other  3(1.0) Unknown  1 (0.3) Lung-RADS Score 1 4318 (60.9)  (%) 2 1246 (17.6) 3 830 (11.7) 4A 539 (7.6)  4B 160 (2.3) 

TABLE 3 Top 10 features for 1 year, 3 year, and 5 year future riskmodels for full cohort. For features categories, a “1” indicates anodule specific feature whereas a “2” indicates a non-nodule specificfeature. Feature ranked 1 year 3 year 5 year based on feature featurefeature importance 1 year features category 3 year category 5 yearcategory 1 Perpendicular 1 Perpendicular 1 Perpendicular 1 diameter oflargest diameter of largest diameter of largest lung nodule lung nodulelung nodule 2 Longest diameter of 1 Longest diameter of 1 Longestdiameter of 1 largest lung nodule largest lung nodule largest lungnodule 3 Margin type of largest 1 Ratio of low 2 Ratio of low 2 lungnodule attenuation area in attenuation area in the upper third of theupper third of the lung to that in the lung to that in the lower thirdthe lower third 4 Lung-RADS 1 Percentage of lung 2 Percentage of lung 2occupied by normal occupied by normal parenchyma parenchyma 5 Percentageof lung 2 Lung-RADS 1 Coronal cross 2 occupied by reticular sectionalarea of features subcutaneous fat 6 Coronal cross sectional 2 Percentageof lung 2 Percentage of lung 2 area of pectoralis minor occupied byoccupied by muscle centrilobular centrilobular emphysema emphysema 7Percentage of lung 2 Coronal cross 2 Axial cross 2 occupied by linearscar sectional area of sectional area of subcutaneous fat subcutaneousfat 8 Axial cross 2 Axial cross 2 Margin type of 1 sectional area ofsectional area of largest lung nodule subcutaneous fat subcutaneous fat9 Ratio of low attenuation 2 Percentage of lung 2 Percentage of lung 2area in the upper third occupied by occupied by of the lung to that insubpleural line reticular features the lower third 10 Axial crosssectional 2 Margin type of 1 Percentage of lung 2 area of pectoralislargest lung occupied by major muscle nodule honeycombing

TABLE 4 Characteristics of the Full Cohort by Cancer Prediction CancerNo Cancer Subgroup Predicted Predicted P Demographic Characteristics n450 6643 Age (Years) 62.9 61.6 <0.001 (mean (SD)) (5.5) (5.0) Gender (%)Female 160 2766 0.013 (35.6) (41.6) Male 290 3877 (64.4) (58.4) Race (%)American 1 (0.2) 23 (0.3) 0.272 Indian or Alaskan Native Asian 4 (0.9)141 (2.1)  Black 10 (2.2)  223 (3.4)  Missing/ 1 (0.2) 11 (0.2) UnknownMore than 4 (0.9) 89 (1.3) One Race Native 2 (0.4) 12 (0.2) Hawaiian orOther Pacific Islander White 428 6128 (95.1) (92.5) Current Current 2353472 1 Smoking Smoker (52.2) (52.3) Status (%) Former 215 3171 Smoker(47.8) (47.7) Pack Years 59.6 55.8 0.001 (mean (SD)) (26.0) (23.8) LungCancer Outcomes Diagnosis with Lung 119 179 <0.001 Lung Cancer Cancer(26.4) (2.7) over 3 Years of No Lung 331 6464 Follow-up (%) Cancer(73.6) (97.3) Lung cancer Stage IA 47 (39.5) 84 (46.9) 0.336 stage (%)Stage IB 16 (13.4) 16 (8.9)  Stage IIA 7 (5.9) 16 (8.9)  Stage IIB 4(3.4) 3 (1.7) Stage IIIA 19 (16.0) 15 (8.4)  Stage IIIB 4 (3.4) 7 (3.9)Stage IV 19 (16.0) 32 (17.9) Unknown/ 3 (2.5) 6 (3.4) Other Lung cancerAdeno- 78 (65.5) 84 (46.9) 0.035 type (%) carcinoma Squamous cell 14(11.8) 38 (21.2) carcinoma Non-small cell 13 (10.9) 17 (9.5)  carcinoma,NOS Small cell 7 (5.9) 24 (13.4) carcinoma Neuro- 5 (4.2) 11 (6.1) endocrine tumor Other 2 (1.7) 4 (2.2) Unknown 0 (0.0) 1 (0.6) NoduleCharacteristics Lung-RADS 1 16 4369 <0.001 Score (%) (3.6) (65.8) 2 1081159 (24.0) (17.4) 3 52 736 (11.6) (11.1) 4A 122 364 (27.1) (5.5) 4B 15215 (33.8) (0.2) Densitometric Measures Low Attenuation 6.5 (7.6) 6.2(9.2) 0.505 Area (Percent of Lung) (mean (SD)) High Attenuation 4.6(1.8) 4.6 (1.8) 0.489 Area (Percent of Lung) (mean (SD)) BodyComposition Pectoralis Minor 1087.8 1105.3 0.308 Cross Axial (322.2)(354.3) Sectional Area (mean (SD)) Pectoralis Major 3047.3 3093.0 0.422Cross Axial (1049.2) (1174.5) Sectional Area (mean (SD)) SubcutaneousFat 5198.9 5674.8 0.001 Axial Cross (2801.1) (2889.1) Sectional Area(mean (SD)) Local Histogram Normal 89.3 (7.8)  89.4 (9.7)  0.914Parenchyma (Percent of Lung) (mean (SD)) Centrilobular 4.2 (7.0) 4.2(8.8) 0.921 Emphysema (Percent of Lung) (mean (SD)) Centrilobular 0.0(0.0) 0.0 (0.0) 0.379 Nodule (Percent of Lung) (mean (SD)) Ground Glass0.0 (0.0) 0.0 (0.0) 0.716 (Percent of Lung) (mean (SD)) Honeycombing 0.1(0.1) 0.1 (0.1) 0.017 (Percent of Lung) (mean (SD)) Linear Scar 0.1(0.1) 0.1 (0.1) 0.528 (Percent of Lung) (mean (SD)) Nodular 0.1 (0.1)0.1 (0.2) 0.287 (Percent of Lung) (mean (SD)) Reticular 5.3 (2.8) 5.1(2.9) 0.166 (Percent of Lung) (mean (SD)) Subpleural Line 0.2 (0.1) 0.2(0.2) 0.048 (Percent of Lung) (mean (SD)) Other 0.0 (0.1) 0.0 (0.1)0.803 Emphysema (Percent of Lung) (mean (SD)) Cyst (Percent 0.0 (0.0)0.0 (0.0) 0.471 of Lung) (mean (SD))

TABLE 5 Enrichment results of 1 year, 3 year, and 5 year future riskmodels for full cohort Area Under the Relative Relative AbsolutePrecision Enroll- Enroll- Enroll- Recall ment ment ment CumulativeMethod Curve Ratio Percentage Percentage Incidence 1 Year Null Model NA1 to 1 100 100 1.79 Random 0.22 17 to 1 6.01 6.01 19.72 Forest 3 YearNull Model NA 1 to 1 100 100 4.20 Random 0.36 16 to 1 6.34 6.34 26.44Forest 5 Year Null Model NA 1 to 1 100 100 5.58 Random 0.40 17 to 1 5.855.85 28.43 Forest ¹ The random forest model is tuned random forest model² The relative enrollment ratio and the relative enrollment percentageare based on the ratio of the number of individuals predicted positiveby the model to the number of individuals in the tested subgroup, inthis case, the entire cohort ³ The absolute enrollment percentage is thepercent of the total cohort who are predicted positive, i.e. suggestedto be enrolled, by the model. ⁴ Note that each model is trainedindependently and so those predicted positive by the 5 year model do notnecessarily include all of those predicted positive by the 3 year model.

TABLE 6 Top 10 features for 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-4A. For features categories, a “1” indicates anodule specific feature whereas a “2” indicates a non-nodule specificfeature. Feature ranked 1 year 3 year 5 year based on feature featurefeature importance 1 year features category 3 year category 5 yearcategory 1 Perpendicular 1 Perpendicular 1 Perpendicular 1 diameter oflargest diameter of largest diameter of largest lung nodule lung nodulelung nodule 2 Longest diameter of 1 Longest diameter of 1 Longestdiameter of 1 largest lung nodule largest lung nodule largest lungnodule 3 Margin type of 1 Margin type of 1 Axial cross 2 largest lungnodule largest lung nodule sectional area of subcutaneous fat 4Lung-RADS 1 Lung-RADS 1 Coronal cross 2 sectional area of subcutaneousfat 5 Axial cross 2 Axial cross 2 Axial cross sectional 2 sectional areaof sectional area of area of pectoralis subcutaneous fat subcutaneousfat minor muscle 6 Coronal cross 2 Coronal cross 2 Ratio of low 2sectional area of sectional area of attenuation area in subcutaneous fatsubcutaneous fat the upper third of the lung to that in the lower third7 Percentage of lung 2 Percentage of lung 2 Percentage of lung 2occupied by occupied by occupied by reticular features reticularfeatures subpleural line 8 Attenuation/density 1 Attenuation/density 1Percentage of lung 2 type of largest lung type of largest lung occupiedby linear nodule nodule scar 9 Percentage of lung 2 Percentage of lung 2Percentage of lung 2 occupied by normal occupied by normal occupied byparenchyma parenchyma centrilobular emphysema 10 Percentage of lung 2Percentage of lung 2 Percentage of lung 2 occupied by linear occupied bylinear occupied by scar scar honeycombing

TABLE 7 Characteristics of the Lung-RADS 1-4A Cohort by CancerPrediction No Cancer Predicted Subgroup Predicted Cancer P DemographicCharacteristics n 392 6531 0 Age (Years) (mean 62.5 61.5 <0.001 (SD))(5.2) (5.1) Gender (%) Female 119 2777 <0.001 (30.4) (42.5) Male 2733754 (69.6) (57.5) Race (%) American Indian 2 15 0.118 or Alaskan Native(0.5) (0.2) Asian 9 124 (2.3) (1.9) Black 4 247 (1.0) (3.8)Missing/Unknown 0 (0.0) 8 (0.1) More than One 6 (1.5) 81 (1.2)  RaceNative Hawaiian 1 (0.3) 17 (0.3)  or Other Pacific Islander White 3706023 (94.4) (92.4) Current Smoking Current Smoker 215 3403 0.316 Status(%) (54.8) (52.1) Former Smoker 177 3128 (45.2) (47.9) Pack Years (mean58.2 55.4 0.022 (SD)) (23.2) (23.6) Lung Cancer Outcomes Diagnosis withLung Cancer 66 161 <0.001 Lung Cancer over 3 (16.8) (2.5) Years ofFollow-up No Lung Cancer 326 6370 (%) (83.2) (97.5) Lung cancer stageStage IA 44 69 0.013 (%) (66.7) (42.9) Stage IB 2 (3.0) 15 (9.3)  StageIIA 5 (7.6) 14 (8.7)  Stage IIB 2 (3.0) 1 (0.6) Stage IIIA 2 (3.0) 19(11.8) Stage IIIB 0 (0.0) 7 (4.3) Stage IV  8 (12.1) 29 (18.0)Unknown/Other 3 (4.5) 7 (4.3) Lung cancer type Adenocarcinoma 43 (65.2)79 (49.1) 0.397 (%) Squamous cell 12 (18.2) 34 (21.1) carcinomaNon-small cell 4 (6.1) 14 (8.7)  carcinoma, NOS Small cell 3 (4.5) 19(11.8) carcinoma Neuroendocrine 3 (4.5) 10 (6.2)  tumor Other 1 (1.5) 4(2.5) Uknown 0 (0.0) 1 (0.6) Nodule Characteristics Lung-RADS 1 71 4276<0.001 Score (%) (18.1) (65.5) 2 72 1176 (18.4) (18.0) 3 69 754 (17.6)(11.5) 4A 180 325 (45.9) (5.0) Densitometric Measures Low Attenuation9.8 5.9 <0.001 Area (Percent of (11.0) (8.7) Lung) (mean (SD)) HighAttenuation 4.1 4.7 <0.001 Area (Percent of (1.5) (2.0) Lung) (mean(SD)) Body Composition Pectoralis Minor 1090.1 1110.9 0.252 Cross Axial(305.3) (352.6) Sectional Area (mean (SD)) Pectoralis Major 2956.23095.2 0.022 Cross Axial (854.1) (1179.1) Sectional Area (mean (SD))Subcutaneous Fat 4478.6 5799.8 <0.001 Axial Cross (2122.2) (2969.3)Sectional Area (mean (SD)) Local Histogram Normal 87.1 89.6 <0.001Parenchyma (11.9) (9.0) (Percent of Lung) (mean (SD)) Centrilobular 7.14.0 <0.001 Emphysema (11.5) (8.2) (Percent of Lung) (mean (SD))Centrilobular 0.0 (0.0) 0.0 (0.0) 0.002 Nodule (Percent of Lung) (mean(SD)) Ground Glass 0.0 (0.0) 0.0 (0.0) 0.096 (Percent of Lung) (mean(SD)) Honeycombing 0.1 (0.2) 0.1 (0.1) 0.138 (Percent of Lung) (mean(SD)) Linear Scar 0.1 (0.1) 0.1 (0.1) 0.003 (Percent of Lung) (mean(SD)) Nodular (Percent of 0.1 (0.3) 0.1 (0.3) 0.094 Lung) (mean (SD))Reticular (Percent 4.6 (2.5) 5.2 (2.9) <0.001 of Lung) (mean (SD))Subpleural Line 0.1 (0.1) 0.2 (0.2) <0.001 (Percent of Lung) (mean (SD))Other Emphysema 0.1 (0.2) 0.0 (0.1) <0.001 (Percent of Lung) (mean (SD))Cyst (Percent of 0.0 (0.0) 0.0 (0.0) 0.588 Lung) (mean (SD))

TABLE 8 Enrichment results of 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-4A Area Under the Relative Relative AbsolutePrecision Enroll- Enroll- Enroll- Recall ment ment ment CumulativeMethod Curve Ratio Percentage Percentage Incidence 1 Year Null Model NA1 to 1 100 100 0.98 Random 0.32 16 to 1 6.37 6.22 7.48 Forest 3 YearNull Model NA 1 to 1 100 100 3.28 Random 0.41 18 to 1 5.66 5.53 16.84Forest 5 Year Null Model NA 1 to 1 100 100 4.65 Random 0.44 16 to 1 6.276.12 17.97 Forest ¹ The random forest model is tuned random forest model² The relative enrollment ratio and the relative enrollment percentageare based on the ratio of the number of individuals predicted positiveby the model to the number of individuals in the tested subgroup, inthis case those with Lung-RADS < 4B ³ The absolute enrollment percentageis the percent of the total cohort who are predicted positive, i.e.suggested to be enrolled, by the model. ⁴ Note that each model istrained independently and so those predicted positive by the 5 yearmodel do not necessarily include all of those predicted positive by the3 year model.

TABLE 9 Top 10 features for 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-3. For features categories, a “1” indicates anodule specific feature whereas a “2” indicates a non-nodule specificfeature. Feature ranked 1 year 3 year 5 year based on feature featurefeature importance 1 year features category 3 year category 5 yearcategory 1 Perpendicular 1 Perpendicular 1 Coronal cross 2 diameter oflargest diameter of largest sectional area of lung nodule lung nodulesubcutaneous fat 2 Margin type 1 Coronal cross 2 Axial cross 2 oflargest sectional area of sectional area of lung nodule subcutaneous fatsubcutaneous fat 3 Axial cross 2 Ratio of low 2 Perpendicular 1sectional area of attenuation area in diameter of largest subcutaneousfat the upper third of lung nodule the lung to that in the lower third 4Percentage of lung 2 Axial cross sectional 2 Percentage of lung 2occupied by linear area of pectoralis occupied by scar minor musclecentrilobular emphysema 5 Ratio of low 2 Axial cross 2 Ratio of low 2attenuation area in sectional area of attenuation area in the upperthird of subcutaneous fat the upper third of the lung to that in thelung to that in the lower third the lower third 6 Percentage of lung 2Percentage of lung 2 Percentage of lung 2 occupied by occupied byoccupied by reticular features subpleural line honeycombing 7 Percentageof lung 2 Longest diameter of 1 Axial cross 2 occupied by normal largestlung nodule sectional area parenchyma of pectoralis major muscle 8Coronal cross 2 Percentage of lung 2 Axial cross 2 sectional areaoccupied by sectional area of lean pectoralis linear scar of leanpectoralis major muscle major muscle 9 Percentage of lung 2 Percentageof lung 2 Percentage of lung 2 occupied by occupied by occupied bynormal subpleural line honeycombing parenchyma 10 Attenuation/density 1Percentage of lung 2 Axial cross sectional 2 type of largest occupied bynormal area of pectoralis lung nodule parenchyma minor muscle

TABLE 10 Characteristics of the Lung-RADS 1-3 Cohort by CancerPrediction No Cancer Predicted Subgroup Predicted Cancer P DemographicCharacteristics n 324 6085 Age (Years) 62.8 61.5 <0.001 (mean (SD))(5.1) (5.0) Gender (%) Female 138 (42.6)  2474 (40.7)  0.527 Male 186(57.4)  3611 (59.3)  Race (%) American Indian 1 (0.3) 16 (0.3) 0.528 orAlaskan Native Asian 5 (1.5) 141 (2.3)  Black 7 (2.2) 232 (3.8) Missing/Unknown 0 (0.0) 10 (0.2) More than One 3 (0.9) 77 (1.3) RaceNative Hawaiian 0 (0.0) 16 (0.3) or Other Pacific Islander White 3085578 (95.1) (91.9) Current Current Smoker 177 3162 0.379 Smoking (54.6)(52.0) Status (%) Former Smoker 147 2923 (45.4) (48.0) Pack Years 59.055.9 0.024 (mean (SD)) (24.2) (23.6) Lung Cancer Outcomes Diagnosis withLung Cancer 50 (15.4) 125 (2.1) <0.001 Lung Cancer No Lung Cancer 2745960 over 3 (84.6) (97.9) Years of Follow-up (%) Lung cancer Stage IA 30(60.0) 59 (47.2) 0.348 stage (%) Stage IB 3 (6.0) 6 (4.8) Stage IIA 3(6.0) 10 (8.0)  Stage IIB 2 (4.0) 1 (0.8) Stage IIIA 3 (6.0) 11 (8.8) Stage IIIB 0 (0.0) 8 (6.4) Stage IV  7 (14.0) 25 (20.0) Unknown/Other 2(4.0) 5 (4.0) Lung cancer Adenocarcinoma 33 53 0.093 type (%) (66.0)(42.4) Squamous cell 8 32 carcinoma (16.0) (25.6) Non-small cell 3 (6.0)18 (14.4) carcinoma, NOS Small cell 2 (4.0) 13 (10.4) carcinomaNeuroendocrine 3 (6.0) 6 (4.8) tumor Other 1 (2.0) 3 (2.4) NoduleCharacteristics Lung-RADS 1 97 4182 <0.001 Score (%) (29.9) (68.7) 2 1481189 (45.7) (19.5) 3 79 714 (24.4) (11.7) Densitometric Measures Low 7.2(8.1) 6.2 (9.1) 0.06 Attenuation Area (Percent of Lung) (mean (SD)) High4.3 (1.2) 4.6 (1.9) 0.002 Attenuation Area (Percent of Lung) (mean (SD))Body Composition Pectoralis 1047.1 1113.3 0.001 Minor Cross (300.0)(349.1) Axial Sectional Area (mean (SD)) Pectoralis 2908.6 3126.1 0.001Major Cross (1101.6) (1196.2) Axial Sectional Area (mean (SD))Subcutaneous 5363.8 5687.1 0.049 Fat Axial (2926.3) (2876.3) CrossSectional Area (mean (SD)) Local Histogram Normal 88.7 89.5 0.119Parenchyma (8.9) (9.3) (Percent of Lung) (mean (SD)) Centrilobular 5.3(8.7) 4.1 (8.4) 0.015 Emphysema (Percent of Lung) (mean (SD))Centrilobular 0.0 (0.0) 0.0 (0.0) 0.938 Nodule (Percent of Lung) (mean(SD)) Ground Glass 0.0 (0.0) 0.0 (0.0) 0.003 (Percent of Lung) (mean(SD)) Honey- 0.1 (0.1) 0.1 (0.1) 0.09 combing (Percent of Lung) (mean(SD)) Linear Scar 0.1 (0.1) 0.1 (0.1) 0.593 (Percent of Lung) (mean(SD)) Nodular 0.0 (0.1) 0.1 (0.3) 0.087 (Percent of Lung) (mean (SD))Reticular 5.1 (2.6) 5.1 (3.0) 0.992 (Percent of Lung) (mean (SD))Subpleural 0.2 (0.1) 0.2 (0.2) 0.897 Line (Percent of Lung) (mean (SD))Other 0.0 (0.1) 0.0 (0.1) 0.074 Emphysema (Percent of Lung) (mean (SD))Cyst (Percent 0.0 (0.0) 0.0 (0.0) 0.574 of Lung) (mean (SD))

TABLE 11 Enrichment results of 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-3 Area Under the Relative Relative AbsolutePrecision Enroll- Enroll- Enroll- Recall ment ment ment CumulativeMethod Curve Ratio Percentage Percentage Incidence 1 Year Null Model NA1 to 1 100 100 0.70 Random 0.35 20 to 1 4.91 4.44 6.03 Forest 3 YearNull Model NA 1 to 1 100 100 2.73 Random 0.43 20 to 1 5.06 4.57 15.43Forest 5 Year Null Model NA 1 to 1 100 100 4.06 Random 0.44 18 to 1 5.665.12 14.88 Forest ¹ The random forest model is tuned random forest model² The relative enrollment ratio and the relative enrollment percentageare based on the ratio of the number of individuals predicted positiveby the model to the number of individuals in the tested subgroup, inthis case those with Lung-RADS < 4 ³ The absolute enrollment percentageis the percent of the total cohort who are predicted positive, i.e.suggested to be enrolled, by the model. ⁴ Note that each model istrained independently and so those predicted positive by the 5 yearmodel do not necessarily include all of those predicted positive by the3 year model.

TABLE 12 Top 10 features for 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-2. For features categories, a “1” indicates anodule specific feature whereas a “2” indicates a non-nodule specificfeature. Feature ranked 1 year 3 year 5 year based on feature featurefeature importance 1 year features category 3 year category 5 yearcategory 1 Longest 1 Coronal cross 2 Axial cross 2 diameter of sectionalarea of sectional area of largest lung subcutaneous fat subcutaneous fatnodule 2 Percentage of 2 Axial cross 2 Ratio of low 2 lung occupied bysectional area of attenuation area in linear scar subcutaneous fat theupper third of the lung to that in the lower third 3 Perpendicular 1Percentage of lung 2 Coronal cross 2 diameter of occupied by sectionalarea of largest lung honeycombing subcutaneous fat nodule 4 Percentageof 2 Ratio of low 2 Axial cross 2 lung occupied by attenuation area inthe sectional area subpleural line upper third of the lung of pectoralisto that in the lower minor muscle third 5 Coronal cross 2 Percentage oflung 2 Axial cross sectional 2 sectional area of occupied by low area ofpectoralis subcutaneous fat attenuation area major muscle 6 Coronalcross 2 Percentage of lung 2 Percentage of lung 2 sectional area ofoccupied by occupied by lean pectoralis linear scar subpleural lineminor muscle 7 Percentage of 2 Axial cross sectional 2 Percentage oflung 2 lung occupied by area of pectoralis occupied by normal minormuscle normal parenchyma parenchyma 8 Coronal cross 2 Perpendicular 1Percentage of 2 sectional area of diameter of largest lung occupiedpectoralis minor lung nodule by linear scar muscle 9 Axial cross 2Longest diameter of 1 Percentage of lung 2 sectional area of largestlung nodule occupied by subcutaneous fat centrilobular emphysema 10Axial cross 2 Percentage of lung 2 Percentage of lung 2 sectional areaof occupied by occupied by low pectoralis minor subpleural lineattenuation area muscle

TABLE 13 Characteristics of the Lung-RADS 1-2 Cohort by CancerPrediction Cancer No Cancer Subgroup Predicted Predicted P DemographicCharacteristics n 294 5314 Age (Years) (mean 62.5 (5.3) 61.4 (5.0)<0.001 (SD)) Gender (%) Female 85 (28.9) 2242 (42.2) <0.001 Male 209(71.1) 3072 (57.8) Race (%) American Indian 3 (1.0) 9 (0.2) 0.056 orAlaskan Native Asian 8 (2.7) 128 (2.4) Black 8 (2.7) 215 (4.1)Missing/Unknown 0 (0.0) 9 (0.2) More than One 4 (1.4) 72 (1.4) RaceNative Hawaiian 0 (0.0) 18 (0.3) or Other Pacific Islander White 271(92.2) 4847 (91.5) Current Smoking Current Smoker 147 (50.0) 2755 (51.8)0.578 Status (%) Former Smoker 147 (50.0) 2559 (48.2) Pack Years (mean59.1 (26.3) 55.5 (24.2) 0.015 (SD)) Lung Cancer Outcomes Diagnosis withLung Cancer 50 (15.4) 125 (2.1) <0.001 Lung Cancer over 3 No Lung Cancer274 (84.6) 5960 (97.9) Years of Follow- up (%) Lung cancer stage StageIA 30 (60.0) 59 (47.2) 0.384 (%) Stage IB 3 (6.0) 6 (4.8) Stage IIA 3(6.0) 10 (8.0) Stage IIB 2 (4.0) 1 (0.8) Stage IIIA 3 (6.0) 11 (8.8)Stage IIIB 0 (0.0) 8 (6.4) Stage IV 7 (14.0) 25 (20.0) Unknown/Other 2(4.0) 5 (4.0) Lung cancer type Adenocarcinoma 11 (52.4) 55 (49.1) 0.973(%) Neuroendocrine 1 (4.8) 5 (4.5) tumor Unknown 0 (0.0) 1 (0.9) Other 0(0.0) 1 (0.9) Squamous cell 5 (23.8) 20 (17.9) carcinoma Non-small cell2 (9.5) 12 (10.7) carcinoma, NOS Small cell carcinoma 2 (9.5) 18 (16.1)Nodule Characteristics Lung- 1 177 (60.2) 4166 (78.4) <0.001 RADS Score(%) 2 117 (39.8) 1148 (21.6) Densitometric Measures Low Attenuation 9.8(11.1) 5.7 (8.4) <0.001 Area (Percent of Lung) (mean (SD)) HighAttenuation 4.2 (1.4) 4.7 (1.9) <0.001 Area (Percent of Lung) (mean(SD)) Body Composition Pectoralis Minor 1065.8 (247.2) 1110.4 (351.3)0.032 Cross Axial Sectional Area (mean (SD)) Pectoralis Major 2965.1(877.5) 3115.9 (1176.4) 0.03 Cross Axial Sectional Area (mean (SD))Subcutaneous Fat 4569.2 (2289.5) 5804.4 (2946.0) <0.001 Axial CrossSectional Area (mean (SD)) Local Histogram Normal 86.7 (11.3) 90.0 (8.8)<0.001 Parenchyma (Percent of Lung) (mean (SD)) Centrilobular 7.7 (11.6)3.7 (7.8) <0.001 Emphysema (Percent of Lung) (mean (SD)) Centrilobular0.0 (0.0) 0.0 (0.0) 0.196 Nodule (Percent of Lung) (mean (SD)) GroundGlass 0.0 (0.0) 0.0 (0.0) 0.637 (Percent of Lung) (mean (SD))Honeycombing 0.1 (0.1) 0.1 (0.1) 0.195 (Percent of Lung) (mean (SD))Linear Scar 0.1 (0.1) 0.1 (0.1) 0.001 (Percent of Lung) (mean (SD))Nodular (Percent 0.0 (0.1) 0.1 (0.2) <0.001 of Lung) (mean (SD))Reticular (Percent 4.9 (3.0) 5.1 (3.0) 0.154 of Lung) (mean (SD))Subpleural Line 0.1 (0.1) 0.2 (0.2) <0.001 (Percent of Lung) (mean (SD))Other Emphysema 0.1 (0.1) 0.0 (0.1) <0.001 (Percent of Lung) (mean (SD))Cyst (Percent of 0.0 (0.0) 0.0 (0.0) 0.46 Lung) (mean (SD))

TABLE 14 Enrichment results of 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1-2 Area Under the Relative Relative AbsolutePrecision Enroll- Enroll- Enroll- Recall ment ment ment CumulativeMethod Curve Ratio Percentage Percentage Incidence 1 Year Null Model NA 1 to 1 100 100 0.43 Random 0.37 17 to 1 6.03 4.77 2.37 Forest 3 YearNull Model NA  1 to 1 100 100 2.37 Random 0.44 19 to 1 5.24 4.14 7.14Forest 5 Year Null Model NA  1 to 1 100 100 3.67 Random 0.46 16 to 16.31 4.99 7.06 Forest ¹ The random forest model is tuned random forestmodel ² The relative enrollment ratio and the relative enrollmentpercentage are based on the ratio of the number of individuals predictedpositive by the model to the number of individuals in the testedsubgroup, in this case those with Lung-RADS <3 ³ The absolute enrollmentpercentage is the percent of the total cohort who are predictedpositive, i.e. suggested to be enrolled, by the model. ⁴ Note that eachmodel is trained independently and so those predicted positive by the 5year model do not necessarily include all of those predicted positive bythe 3 year model.

TABLE 15 Top 10 features for 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1. For features categories, a “1” indicates anodule specific feature whereas a “2” indicates a non-nodule specificfeature. Feature ranked 1 year 3 year 5 year based on feature featurefeature importance 1 year features category 3 year category 5 yearcategory 1 Percentage of lung 2 Coronal cross 2 Ratio of low 2 occupiedby normal sectional area of attenuation area in parenchyma subcutaneousfat the upper third of the lung to that in the lower third 2 Percentageof lung 2 Percentage of 2 Percentage of lung 2 occupied by lung occupiedby occupied by centrilobular low attenuation honeycombing emphysema area3 Axial cross 2 Percentage of 2 Axial cross sectional 2 sectional areaof lung occupied by area of pectoralis subcutaneous fat centrilobularminor muscle emphysema 4 Coronal cross 2 Percentage of 2 Percentage oflung 2 sectional area of lung occupied by occupied by low subcutaneousfat linear scar attenuation area 5 Percentage of lung 2 Axial cross 2Percentage of lung 2 occupied by sectional area of occupied by reticularfeatures pectoralis minor centrilobular muscle emphysema 6 Percentage oflung 2 Percentage of 2 Axial cross sectional 2 occupied by lung occupiedby area of pectoralis subpleural line emphysematous major musclefeatures 7 Sagittal cross 2 Percentage of 2 Percentage of lung 2sectional area of lung occupied by occupied by linear pectoralis minornormal scar muscle parenchyma 8 Coronal cross 2 Axial cross 2 Percentageof lung 2 sectional area of sectional area of occupied by normalpectoralis minor subcutaneous fat parenchyma muscle 9 Coronal cross 2Ratio of low 2 Percentage of lung 2 sectional area of attenuation areaoccupied by high lean pectoralis in the upper third attenuation areaminor muscle of the lung to that in the lower third 10 Coronal cross 2Percentage of 2 Percentage of lung 2 sectional area of lung occupied byoccupied by lean pectoralis honeycombing subpleural line major muscle

TABLE 16 Characteristics of the Lung-RADS 1 Cohort by Cancer PredictionCancer No Cancer Subgroup Predicted Predicted P DemographicCharacteristics n 261 4077 Age (Years) (mean 63.7 (5.5) 61.1 (4.8)<0.001 (SD)) Gender (%) Female 35 (13.4) 1698 (41.6) <0.001 Male 226(86.6) 2379 (58.4) Race (%) American Indian 1 (0.4) 10 (0.2) 0.002 orAlaskan Native Asian 15 (5.8) 89 (2.2) Black 5 (1.9) 179 (4.4)Missing/Unknown 0 (0.0) 10 (0.2) More than One 5 (1.9) 52 (1.3) RaceNative Hawaiian 2 (0.8) 11 (0.3) or Other Pacific Islander White 232(89.2) 3718 (91.4) Current Smoking Current Smoker 127 (48.7) 2108 (51.7)0.373 Status (%) Former Smoker 134 (51.3) 1969 (48.3) Pack Years (mean61.0 (22.8) 55.3 (23.5) <0.001 (SD)) Lung Cancer Outcomes Diagnosis withLung Cancer 50 (15.4) 125 (2.1) <0.001 Lung Cancer over 3 No Lung Cancer274 (84.6) 5960 (97.9) Years of Follow- up (%) Lung cancer stage StageIA 30 (60.0) 59 (47.2) 0.384 (%) Stage IB 3 (6.0) 6 (4.8) Stage IIA 3(6.0) 10 (8.0) Stage IIB 2 (4.0) 1 (0.8) Stage IIIA 3 (6.0) 11 (8.8)Stage IIIB 0 (0.0) 8 (6.4) Stage IV 7 (14.0) 25 (20.0) Unknown/Other 2(4.0) 5 (4.0) Lung cancer type Adenocarcinoma 5 (35.7) 34 (39.5) 0.677(%) Squamous cell 5 (35.7) 23 (26.7) carcinoma Non-small cell 2 (14.3)11 (12.8) carcinoma, NOS Small cell carcinoma 1 (7.1) 9 (10.5)Neuroendocrine 0 (0.0) 7 (8.1) tumor Unknown 1 (7.1) 1 (1.2) Other 0(0.0) 1 (1.2) Nodule Characteristics Lung-RADS Score 1 261 (100) 4077(100) N/A (%) Densitometric Measures Low Attenuation 16.3 (12.3) 5.6(8.6) <0.001 Area (Percent of Lung) (mean (SD)) High Attenuation 3.9(1.1) 4.7 (2.0) <0.001 Area (Percent of Lung) (mean (SD)) BodyComposition Pectoralis Minor 1029.8 (229.1) 1123.5 (357.5) <0.001 CrossAxial Sectional Area (mean (SD)) Pectoralis Major 2993.4 (697.1) 3140.3(1207.9) 0.052 Cross Axial Sectional Area (mean (SD)) Subcutaneous Fat3850.7 (1643.8) 5729.3 (2928.9) <0.001 Axial Cross Sectional Area (mean(SD)) Local Histogram Normal 80.2 (12.3) 90.0 (8.7) <0.001 Parenchyma(Percent of Lung) (mean (SD)) Centrilobular 14.3 (13.0) 3.6 (7.7) <0.001Emphysema (Percent of Lung) (mean (SD)) Centrilobular 0.0 (0.0) 0.0(0.0) <0.001 Nodule (Percent of Lung) (mean (SD)) Ground Glass 0.0 (0.0)0.0 (0.0) 0.217 (Percent of Lung) (mean (SD)) Honeycombing 0.1 (0.1) 0.1(0.1) 0.431 (Percent of Lung) (mean (SD)) Linear Scar 0.1 (0.1) 0.1(0.1) 0.07 (Percent of Lung) (mean (SD)) Nodular (Percent 0.0 (0.0) 0.1(0.3) <0.001 of Lung) (mean (SD)) Reticular (Percent 4.8 (2.9) 5.2 (3.0)0.039 of Lung) (mean (SD)) Subpleural Line 0.1 (0.1) 0.2 (0.2) <0.001(Percent of Lung) (mean (SD)) Other Emphysema 0.1 (0.2) 0.0 (0.1) <0.001(Percent of Lung) (mean (SD)) Cyst (Percent of 0.0 (0.0) 0.0 (0.0) 0.854Lung) (mean (SD))

TABLE 17 Enrichment results of 1 year, 3 year, and 5 year future riskmodels for Lung-RADS 1 Area Under the Relative Relative AbsolutePrecision Enroll- Enroll- Enroll- Recall ment ment ment CumulativeMethod Curve Ratio Percentage Percentage Incidence 1 Year Null Model NA1 to 1 100 100 0.35 Random 0.46 14 to 1 7.10 4.34 0.32 Forest 3 YearNull Model NA 1 to 1 100 100 2.31 Random 0.45 17 to 1 6.02 3.68 5.36Forest 5 Year Null Model NA 1 to 1 100 100 3.67 Random 0.47 17 to 1 5.763.52 6.80 Forest ¹ The random forest model is tuned random forest model² The relative enrollment ratio and the relative enrollment percentageare based on the ratio of the number of individuals predicted positiveby the model to the number of individuals in the tested subgroup, inthis case those with Lung-RADS <2 ³ The absolute enrollment percentageis the percent of the total cohort who are predicted positive, i.e.suggested to be enrolled, by the model. ⁴ Note that each model istrained independently and so those predicted positive by the 5 yearmodel do not necessarily include all of those predicted positive by the3 year model.

TABLE 18 Performance of risk prediction models incorporating non-nodulespecific features and nodule specific features (radiomic features)Incidence Time Rate Period Lung- Training/ True True False False without(year) RADS Testing Positives Negatives Positives Negatives SensitivitySpecificity PPV NPV Enrichment 1   1-4B Training Set 108 6899 449 270.8000 0.9389 0.1939 0.9961 0.0180 1   1-4B Testing Set 76 6814 548 450.6281 0.9256 0.1218 0.9934 0.0162 1   1-4A Training Set 35 6573 426 320.5224 0.9391 0.0759 0.9952 0.0095 1   1-4A Testing Set 18 6515 423 390.3158 0.9390 0.0408 0.9940 0.0081 1  1-3 Training Set 41 4139 1929 70.8542 0.6821 0.0208 0.9983 0.0078 1  1-3 Testing Set 33 4120 1893 90.7857 0.6852 0.0171 0.9978 0.0069 1   2-4B Training Set 84 3325 153 50.9438 0.9560 0.3544 0.9985 0.0250 1   2-4B Testing Set. 66 3304 241 170.7952 0.9320 0.2150 0.9949 0.0229 1 4A-4B Training Set 79 1252 28 80.9080 0.9781 0.7383 0.9937 0.0636 1 4A-4B Testing Set 55 1281 68 240.6962 0.9496 0.4472 0.9816 0.0553 3   1-4B Training Set 247 6395 775 660.7891 0.8919 0.2417 0.9898 0.0418 3   1-4B Testing Set 155 6282 896 1500.5082 0.8752 0.1475 0.9767 0.0408 3   1-4A Training Set 167 5384 146352 0.7626 0.7863 0.1025 0.9904 0.0310 3   1-4A Testing Set 101 5276 1508110 0.4787 0.7777 0.0628 0.9796 0.0302 3  1-3 Training Set 104 4786 116957 0.6460 0.8037 0.0817 0.9882 0.0263 3  1-3 Testing Set 59 4732 1180 840.4126 0.8004 0.0476 0.9826 0.0236 3   2-4B Training Set 199 3001 355 120.9431 0.8942 0.3592 0.9960 0.0592 3   2-4B Testing Set 122 2951 459 960.5596 0.8654 0.2100 0.9685 0.0601 3 4A-4B Training Set 137 1029 186 150.9013 0.8469 0.4241 0.9856 0.1112 3 4A-4B Testing Set 105 1000 266 570.6481 0.7899 0.2830 0.9461 0.1134

TABLE 19 Top 10 features for 1 year and 3 year future risk models forLung-RADS 1-4B subjects using radiomic features. For featurescategories, a “1” indicates a radiomic feature (e.g., nodule specificfeature) whereas a “2” indicates a non-nodule specific feature. Featureranked 1 year 3 year based on feature feature importance 1 year featurescategory 3 year features category 1 application of wavelet filter low- 1application of wavelet filter low-low-low 1 high-high neighboring graytone gray level co-occurrence matrix joint difference matrixcoarseness - energy - edge volume of nodule edge volume of nodule 2application of gaussian filter 1 application of gaussian filter 1 (sigma= 1 mm) 3d neighboring (sigma = 1 mm) 3d neighboring gray gray tonedifference matrix tone difference matrix coarseness - coarseness - edgevolume boundary volume of nodule of nodule 3 Axial cross sectional areaof 2 original (no filter) neighboring gray tone 1 subcutaneous fatdifference matrix coarseness - boundary volume of nodule 4 applicationof wavelet filter low- 1 application of wavelet filter low-high-low 1low-low gray level co-occurrence gray level co-occurrence matrix matrixjoint energy - interior information measure of correlation volume ofnodule 1 - edge volume of nodule 5 Normal parenchyma - percentage 2Axial cross sectional area of 2 of lung subcutaneous fat 6Emphysematous - percentage of 2 Upper lower third low attenuation 2 lungarea ratio 7 application of wavelet filter high- 1 application ofwavelet filter low-low-high 1 high-high neighboring gray tone gray leveldependence matrix small difference matrix coarseness - dependence lowgray level emphasis - edge volume of nodule interior volume of nodule 8application of wavelet filter low- 1 Honeycombing - percentage of lung 2low-high neighboring gray tone difference matrix coarseness - boundaryvolume of nodule 9 application of gaussian filter 1 Coronal crosssectional area 2 (sigma = 1 mm) 3d neighboring of subcutaneous fat graytone difference matrix coarseness - boundary volume of nodule 10application of wavelet filter low- 1 Lung-RADS 2 low-high neighboringgray tone difference matrix coarseness - edge volume of nodule

TABLE 20 Top 10 features for 1 year and 3 year future risk models forLung-RADS 1-4A subjects using radiomic features. For featurescategories, a “1” indicates a radiomic feature (e.g., nodule specificfeature) whereas a “2” indicates a non-nodule specific feature. Featureranked 1 year 3 year based on feature feature importance 1 year featurescategory 3 year features category 1 Axial cross sectional area of 2application of wavelet filter low-high-low 1 subcutaneous fatneighboring gray tone difference matrix coarseness - boundary volume ofnodule 2 Coronal cross sectional area of 2 application of wavelet filterlow-high-high 1 subcutaneous fat neighboring gray tone difference matrixcoarseness - edge volume of nodule 3 normal parenchyma - percentage 2application of gaussian filter (sigma = 1 mm) 1 of lung 3d neighboringgray tone difference matrix coarseness - edge volume of nodule 4application of wavelet filter high- 1 original (no filter) gray levelco-occurrence 1 low-high first order mean - matrix information measureof correlation boundary volume of nodule 1 - boundarv volume of nodule 5application of wavelet filter low- 1 Coronal cross sectional area of 2high-high gray level size zone subcutaneous fat matrix small area lowgray level emphasis - interior volume of nodule 6 nodular - percentageof lung 2 Axial cross sectional area of subcutaneous 2 fat 7 applicationof wavelet filter high- 1 application of wavelet filter low-high-low 1high-high gray level co-occurrence neighboring gray tone differencematrix matrix cluster shade - interior coarseness - edge volume ofnodule volume of nodule 8 application of wavelet filter low- 1application of wavelet filter low-low-low 1 low-high first orderuniformity - gray level run length matrix run variance - interior volumeof nodule boundary volume of nodule 9 honeycombing - percentage of 2original (no filter) gray level size zone 1 lung matrix gray level nonuniformity normalized - boundary volume of nodule 10 application ofwavelet filter low- 1 application of wavelet filter high-high-high 1low-low neighboring gray tone gray level co-occurrence matrix clusterdifference matrix coarseness - shade - interior volume of noduleboundary volume of nodule

TABLE 21 Top 10 features for 1 year and 3 year future risk models forLung-RADS 1-3 subjects using radiomic features. For features categories,a “1” indicates a radiomic feature (e.g., nodule specific feature)whereas a “2” indicates a non-nodule specific feature. Feature ranked 1year 3 year based on feature feature importance 1 year features category3 year features category 1 Axial cross sectional area of 2 Axial crosssectional area of subcutaneous 2 subcutaneous fat fat 2 Coronal crosssectional area of 2 Coronal cross sectional area of 2 subcutaneous fatsubcutaneous fat 3 Whole lung normal parenchyma 2 application of waveletfilter low-low-low 1 type fraction gray level run length matrix runvariance - boundary volume of nodule 4 wavelet hlh first order mean 1application of gaussian filter 1 boundary (sigma = 1 mm) 3d gray levelco-occurrence matrix information measure of correlation 1 - boundaryvolume of nodule 5 wavelet lhh glszm small area low 1 normalparenchyma - percentage of lung 2 gray level emphasis interior 6 Wholelung nodular type fraction 2 emphysematous - percentage of lung 2 7wavelet, hhh glem cluster shade 1 honeycombing - percentage of lung 2interior 8 wavelet llh firstorder uniformity 1 centrilobular emphysema -percentage of 2 interior lung 9 wholelung honeycombing type 2 upperlower third low attenuation area ratio 2 fraction 10 wavelet lll ngtdmcoarseness 1 reticular percentage of lung 2 boundary

TABLE 22 Top 10 features for 1 year and 3 year future risk models forLung-RADS 2-4B subjects using radiomic features. For featurescategories, a “1” indicates a radiomic feature (e.g., nodule specificfeature) whereas a “2” indicates a non-nodule specific feature. Featureranked 1 year 3 year based on feature feature importance 1 year featurescategory 3 year features category 1 application of wavelet filter high-1 application of gaussian filter (sigma = 1 mm) 1 low-low neighboringgray tone 3d neighboring gray tone difference matrix difference matrixcoarseness - coarseness - boundary volume of nodule edge volume ofnodule 2 application of gaussian filter 1 application of wavelet filterlow-low-low 1 (sigma = 1 mm) 3d neighboring gray gray levelco-occurrence matrix joint tone difference matrix coarseness - energy -edge volume of nodule edge volume of nodule 3 application of waveletfilter low- 1 original (no filter) neighboring gray tone 1 low-highneighboring gray tone difference matrix coarseness - boundary-difference matrix coarseness - volume of nodule boundary volume ofnodule 4 application of wavelet filter low- 1 application of waveletfilter low-high-low 1 low-low gray level size zone gray levelco-occurrence matrix matrix low gray level zone information measure ofcorrelation 1 - edge emphasis - interior volume of volume of nodulenodule 5 application of gaussian filter 1 application of wavelet filterhigh-low-low 1 (sigma = 1 mm) 3d neighboring gray- neighboring gray tonedifference matrix tone difference matrix coarseness - coarseness - edgevolume of nodule boundary volume of nodule 6 application of waveletfilter low- 1 application of wavelet filter low-low-high 1 low-high graylevel dependence gray level dependence matrix small matrix smalldependence low gray dependence low gray level emphasis - levelemphasis - interior volume of interior volume of nodule nodule 7application of wavelet filter low- 1 original (no filter) gray levelco-occurrence 1 low-high first order uniformity - matrix informationmeasure of correlation interior volume of nodule 1 - boundary volume ofnodule 8 application of wavelet filter low- 1 application of waveletfilter high-low-low 1 low-low gray level dependence gray levelco-occurrence matrix maximum matrix small dependence low grayprobability - edge volume of nodule level emphasis - interior volume ofnodule 9 application of wavelet filter low- 1 application of gaussianfilter (sigma = 1 mm) 1 high-high neighboring gray tone 3d gray levelco-occurrence matrix difference matrix coarseness - information measureof correlation 1 - edge volume of nodule boundary volume of nodule 10application of wavelet filter high- 1 Upper lower third low attenuationarea 2 high-high gray level dependence ratio matrix small dependence lowgray level emphasis - interior volume of nodule

TABLE 23 Top 10 features for 1 year and 3 year future risk models forLung-RADS 4A-4B subjects using radiomic features. For featurescategories, a “1” indicates a radiomic feature (e.g., nodule specificfeature) whereas a “2” indicates a non-nodule specific feature. Featureranked 1 year 3 year based on feature feature importance 1 year featurescategory 3 year features category 1 application of wavelet filter high-1 application of wavelet filter low-high-low 1 low-low neighboring graytone gray level co-occurrence matrix difference matrix coarseness -information measure of correlation 1 - edge edge volume of nodule volumeof nodule 2 application of gaussian filter 1 application of waveletfilter low-low-low 1 (sigma=1mm) 3d neighboring gray- gray levelco-occurrence matrix joint tone difference matrix coarseness - energy -edge volume of nodule edge volume of nodule 3 application of waveletfilter low- 1 original (no filter) neighboring gray tone 1 high-low graylevel co-occurrence difference matrix coarseness - boundary- matrixinformation measure of volume of nodule correlation 1 - edge volume ofnodule 4 application of wavelet filter low- 1 application of gaussianfilter (sigma = 1 mm) 1 low-high neighboring gray tone 3d neighboringgray tone difference matrix coarseness - boundary volume of noduledifference matrix coarseness - boundary volume of nodule 5 applicationof wavelet filter low- 1 application of wavelet filter low-low-high 1low-high first order uniformity - gray level co-occurrence matrixinterior volume of nodule information measure of correlation 1 - edgevolume of nodule 6 application of gaussian filter 1 application of wavelet filter low-low-low 1 (sigma- 1mm) 3d neighboring gray neighboringgray tone difference matrix tone difference matrix coarsenesscoarseness - boundary volume of nodule boundary volume of nodule 7application of wavelet filter low- 1 application of wavelet filterlow-low-high 1 low-low gray level size zone gray level dependence matrixsmall matrix low gray level zone dependence low gray level emphasis -emphasis - interior volume of interior volume of nodule nodule 8application of wavelet filter low- 1 upper lower third low attenuationarea ratio 2 low-high gray level dependence matrix small dependence lowgray level emphasis - interior volume of nodule 9 application of waveletfilter high- 1 application of wavelet filter high-low-low 1 low-low graylevel size zone neighboring gray tone difference matrix matrix low graylevel zone coarseness - edge volume of nodule emphasis - interior volumeof nodule 10 application of wavelet filter low- 1 application of waveletfilter high-low-low 1 high-high neighboring gray tone gray levelco-occurrence matrix maximum difference matrix coarseness -probability - edge volume of nodule edge volume of nodule

What is claimed is:
 1. A method for predicting one or more future risksof lung cancer for a subject, the method comprising: obtaining one ormore images captured from the subject at a single timepoint; extractingfeatures from the one or more obtained images, the extracted featurescomprising at least non-nodule specific features, wherein the non-nodulespecific features comprise both lung parenchyma features and bodycomposition features, wherein the body composition features comprise afeature of a pectoralis muscle or subcutaneous fat of the subject;predicting one or more future risks of lung cancer for the subject byapplying one or more trained risk prediction models to analyze theextracted features from the one or more obtained images.
 2. The methodof claim 1, wherein the lung parenchyma features comprise one or more ofdensitometric measures of the lung parenchyma or local histogrammeasures of the lung parenchyma.
 3. The method of claim 2, wherein thedensitometric measures of the lung parenchyma comprise one or more of:percentage of low attenuation area of the lung, percentage of highattenuation area of the lung, and ratio of low attenuation or highattenuation area in an upper lung zone in comparison to a lower lungzone.
 4. The method of claim 2, wherein the local histogram measures ofthe lung parenchyma comprise one or more percentage(s) of lung occupiedby any of normal tissue, centrilobular emphysema, centrilobular nodule,ground glass, honeycombing, linear scar, nodular, reticular, subpleuralline, other emphysema, or cyst.
 5. The method of claim 1, wherein thefeature of the pectoralis muscle or subcutaneous fat comprises apectoralis cross-sectional area, pectoralis lean cross-sectional area,or sub-cutaneous fat cross-sectional area.
 6. The method of claim 1,wherein the extracted features further comprise nodule specific featuresselected from one or more of nodule specific attenuation, nodule margindescription, nodule size, nodule shape, nodule texture, nodule diameter,Lung-RADS score, or radiomic features.
 7. The method of claim 6, whereinradiomic features comprise one or more of first order statistics, 3Dshape based features, 2D shape based features, gray level cooccurrencematrix, gray level run length matrix, gray level size zone matrix,neighboring gray tone difference matrix, and gray level dependencematrix.
 8. The method of claim 1, wherein predicting the one or morefuture risks of lung cancer for the subject comprises applying a M riskprediction model to predict whether the subject is likely to developlung cancer within M.
 9. The method of claim 8, wherein M is any one of6 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 3.5 years, 4years, 4.5 years, 5 years, 5.5 years, 6 years, 6.5 years, 7 years, 7.5years, 8 years, 8.5 years, 9 years, 9.5 years, 10 years, 10.5 years, 11years, 11.5 years, 12 years, 12.5 years, 13 years, 13.5 years, 14 years,14.5 years, 15 years, 15.5 years, 16 years, 16.5 years, 17 years, 17.5years, 18 years, 18.5 years, 19 years, 19.5 years, or 20 years.
 10. Themethod of claim 8, wherein the M risk prediction model comprises nodulespecific features and non-nodule specific features, wherein greater than50% of the top 3 extracted features with the highest feature importancevalues are non-nodule specific features.
 11. The method of claim 8,wherein the M risk prediction model comprises nodule specific featuresand non-nodule specific features, wherein greater than 50% of the top 5extracted features with the highest feature importance values arenon-nodule specific features.
 12. The method of claim 8, wherein the Mrisk prediction model comprises nodule specific features and non-nodulespecific features, wherein greater than 50% of the top 10 extractedfeatures with the highest feature importance values are non-nodulespecific features.
 13. The method of claim 8, wherein the M riskprediction model achieves a performance metric of an area under a curve(AUC) value of at least 0.57.
 14. The method of claim 1, wherein one ofthe one or more risk prediction models is trained to predict the futurerisk of lung cancer using training images captured from a trainingcohort comprising training individuals classified in any of Lung-RADS1-4B, training individuals classified in any of Lung-RADS 1-4A, trainingindividuals classified in any of Lung-RADS 1-3, training individualsclassified in any of Lung-RADS 1-2, or training individuals classifiedin any of Lung-RADS
 1. 15. The method of claim 14, wherein a majority oftraining individuals in the training cohort are previously classified inLung-RADS
 1. 16. The method of claim 1, wherein the one or more imagescomprises are thoracic CT images or chest X-ray images.
 17. The methodof claim 1, further comprising: prior to predicting one or more futurerisks of lung cancer for the subject: obtaining nodule-specific featurescorresponding to the subject; determining that the subject is acandidate for future risk prediction based on the nodule-specificfeatures.
 18. The method of claim 1, wherein the lung cancer is eithernon-small cell lung cancer or small cell lung cancer.
 19. The method ofclaim 1, further comprising selecting a clinical response for thesubject based on the predicted future risk of lung cancer.
 20. Anon-transitory computer readable medium for predicting one or morefuture risks of lung cancer for a subject, the non-transitory computerreadable medium comprising instructions that, when executed by aprocessor, cause the processor to: obtain one or more images capturedfrom the subject at a single timepoint; extract features from the one ormore obtained images, the extracted features comprising at leastnon-nodule specific features, wherein the non-nodule specific featurescomprise both lung parenchyma features and body composition features,wherein the body composition features comprise a feature of a pectoralismuscle or subcutaneous fat of the subject; predict one or more futurerisks of lung cancer for the subject by applying one or more trainedrisk prediction models to analyze the extracted features from the one ormore obtained images.